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QuaDRiGa - Quasi Deterministic Radio Channel Generator, User

1. N 19 2 W D e QuaDRiGa v1 2 3 307 A TUTORIALS rmsds Columns 1 through 7 48 8391 6 8370 55 2154 48 7273 25 9658 29 7929 22 2436 Columns 8 through 11 68 7237 13 3159 85 5425 121 5296 average 47 8848 Generate channel coefficients Next we generate the channel coefficients This is a lengthy task The next line then combines the channels of the individual segments into a time continuous channel Here the parameter 0 2 sets the length of the overlap region between two segments In this case it is 20 c p get_channels h Generate coefficients cn c merge 0 2 Combine segments Channels 00000000000000000000000000000000000000000000000000 J 21 seconds Merging 00000000000000000000000000000000000000000000000000 1 seconds Evaluation of the data The next two plots show some basic evaluations of the generated coefficients The first plot shows the received power for the 4 MIMO links along the track The plot shows the differences between the LOS and NLOS segments and the cross pol discrimination between the MIMO links The average path loss for LOS was set to 95 dB and for NLOS 113 dB dist 1 cn no_snap t get_length cn no_snap ind find strcmp t scenario MIMOSA_10 45_L0S los i for n 1 numel ind start t segment_index ind n if n numel ind try stop t segment_index ind n 1 catch stop t no_snapshots end else stop t segment_index ind n
2. p 1 create_parameter_sets 0 Each parameter set has two different kinds of parameters One for the scenario and one for the current state For example a scenario might have an average RMS Delay spread of 158 ns plus a certain variance which defines a range for the RMSDS In addition to that there are cross correlations with other parameters such as the angular spread at the transmitter All those parameters are stored in the scenpar property For the good state that parameters are S1 stremp p 1 name p 2 name MIMOSA 10 45 LOS_Tx1 X Select good state p S1 scenpar 4 Show parameter list ans NumClusters 8 r_DS 2 5000 PerClusterAS_D 6 2000e 07 PerClusterAS_A 12 PerClusterES_D 1 9000e 07 PerClusterES_A 7 LOS_scatter_radius 0 1000 LNS_ksi 3 xpr_mu 11 9000 xpr_sigma 5 5000 Copyright Fraunhofer Heinrich Hertz Institute 85 eMail quadriga hhi fraunhofer de yee Bee FOOMA NIAAA e wh Ns 00 NNN NN NNN WD h o i 39 wn re N N QuaDRiGa v1 2 3 307 A TUTORIALS DS_mu 7 5000 DS_sigma 0 3000 AS_D_mu 4 6000 AS_D_sigma 0 1000 AS_A_mu 5000 AS_A_sigma 0 2000 ES_D_mu 5 1200 ES_D_sigma 0 1000 ES_A_mu 4000 ES_A_sigma 0 1000 F_sigma 3 6000 KF_mu 5 5000 KF_sigma 5 9000 DS_lambda 30 5000 AS_D_lambda 000 AS_A_lambda 31 5000 ES_D_lambda 000 wn ES_A_lambda 6 SF_lambda 35 KF_lambda 4 5000 asD_ds 0
3. Figure 6 WINNER system level approach showing several segments drops Source KMH 07 as seen from the MT as well as the subpath delays change during a drop due to the MT movement Similarly the LOS directions between BS and MT and the polarization characteristics vary in time Implementation details of the drifting can be found in Section 3 2 3 2 Drifting of Shadow Fading and K Factor The time evolution of shadow fading is determined by its spatial autocorrelation function References show that an exponential function fits well and the drifting can thus be modeled by a first order autoregressive process This is realized by calculating maps containing the correlated large scale parameters The drifting is then implemented by reading the map values along the segments track and interpolating the data 3 1 2 Sample Density vs Sample Rate If you consider moving receivers choosing the right sampling rate depends directly on the Doppler spectrum In order to successfully reconstruct the impulse response you need a sample rate fr fulfilling the sampling theorem fr gt 2Bp 4max Afp ome 3 with Bp being the width of the Doppler spectrum Afp the frequency change due to velocity v and Ae being the carrier wavelength Thus the appropriate sampling rate is proportional to the maximal velocity of the receivers Since it is sometimes useful to examine algorithm behavior for different user velocities it is unfortunate to fix the
4. Copyright Fraunhofer Heinrich Hertz Institute 23 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE use_subpath_output Return all 20 subpaths By default the complex valued amplitudes of 20 sub paths are summed up to calculate the path amplitude Setting use_subpath_output to 1 returns the individual amplitudes of each subpath in channel coeff at the output of the model show_progress_bars center_frequency Show a progress bar on the MATLAB prompt Center frequency in Hz map_resolution Resolution of the decorrelation maps in samples m version Version number of the current QuaDRiGa release constant speed_of_light Speed of light constant wavelength Carrier wavelength in m read only Methods h_simpar simulation_parameters Description Creates a new simulation_parameters object with default settings set_speed speed_kmh sampling _rate_s Description This method can be used to automatically calculate the sample density for a given mobile speed Input speed_kmh speed in km h sampling_rate_s channel update rate in s Copyright Fraunhofer Heinrich Hertz Institute 24 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 2 Class array This class combines all functions to create and edit antenna arrays An antenna array is a set of single antenna elements each
5. Drifting SF Drifting KF Delay factor No clusters Angular Spread K Factor Delay spread K Factor Generate initial Generate cluster delays powers Cluster powers Generate drifting AoAs Drifting delay for each path Channel coefficients Initial delay for each path y Constant values Apply speed profile P One update per segment t hot or Sea EX Figure 4 QuaDRiGa Data Flow Copyright Fraunhofer Heinrich Hertz Institute AT eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 4 Scenario Specific Parameters The large scale parameters LSPs are defined by the parameter files which can be found in the folder config of the QuaDRiGa Core source folder The parameters are processed as follows e The states and segments are identified by a name Examples are S1 S2 MIMOSA 10 45 LOS parameter set selected for good state gt MIMOSA_10 45_NLOS parameter set selected for bad state e The name selects the related configuration file For the given example the files MIMOSA_10 45_LOS conf and MIMOSA_10 45_NLOS conf are selected Table 9 Parameter sets provided together with the standard software LOSonly One LOS Path only no Shadow Fading no Path Loss WINNER_UMa_C2_LOS WINNER_UMa_C2_NLOS WINNER Urban Macrocell For typical terrestrial base stations deployed above rooftop in den
6. the Rx 45 and 90 is rotated around the x axis This is because the movement direction figure 4 New figure plot abs squeeze c coeff 3 1 72 axis 0 360 0 1 1 legend Re O cire Raz 6 cire Rzi SO cire xlabel Position degrees ylabel LOS Power linear scale title x lt SO ciee Be Oieiee 6icite 9O ecire When the receiver is vertical blue line both antennas are always crossed There is no position around the circle where a good link can be established When the receiver is horizontal red line however there are two points where the two dipoles are aligned For the 45 dipole the same behavior can be observed but with roughly half the power close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 Copyright Fraunhofer Heinrich Hertz Institute 106 eMail quadriga hhi fraunhofer de 10 11 14 QuaDRiGa v1 2 3 307 A TUTORIALS A 8 Visualizing RHCP LHCP Patterns The internal algorithms of the channel model only work with linear polarization The antenna patterns are thus only stored in H V polarization This script defines two circular patch antennas and places them in an environment It then rotates the transmit antenna degree by degree and thus samples all azimuth and elevation angles The channel model is set up to record the channel response and thus record the RHCP LH
7. 7 to 7 elevation_grid Elevation angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provides the elevation sampling angles in radians ranging from 4 downwards to 5 upwards visualize element Description Create a plot showing the element configurations Input element The element numbers for which the plot os created If no element number s are given a plot is created for each element in the array Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 29 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 3 Class track One feature of the channel model is the continuous evolution of wireless channels when the terminal moves through the environment A track describes the movement of a mobile terminal It is composed of an ordered list of positions During the simulation one snapshot is generated for each position on the track Along the track wireless reception conditions may change e g when moving from an area with LOS toa shaded area This behavior is described by segments or states A segment is a subset of positions that have similar reception conditions Each segment is classified by a segment index i e the center position of the segment and a scenario The scenario must be one of the supported scenarios in class parameter_set Properties nam
8. Description Determines links for which channel coefficient are generated This function can be used to automatically determine the links for which channel coefficients should be generated For example in a large network there are multiple base stations and mobile terminals The base stations however only serve a small area It the terminal is far away from this area it will receive only noise from this particular BS In this case the channel coefficients will have very little power and do not need to be calculated Disabling those links can reduce the computation time and the storage requirements for the channel coefficients significantly There are several methods to du this which can be selected by the input variable method Methods all Enables the simulation of all links power Calculates the expected received power taking into account the path loss the antenna patterns the LOS polarization and the receiver orientation If the power of a link is below the threshold it gets deactivated sf Same as power but this option also includes the shadow fading Therefore the LSPs have to be calculated LSPs get then stored in layout track par This method is the most accurate The actual power in the channel coefficients can be up to 6 dB higher due to multipath effects Input method Link selection method Supported are all power and sf see above threshold If the Rx power is belo
9. parameter_set The LOS polarization is calculated from the geometric orientation of the antennas A core function here is the interpolation of the antenna patterns which results in a specific H and V value for each subpath The core function then generates the coefficients themselves This is done for each antenna element and for each snapshot separately and also includes the Doppler shift of each subpath Finally the K factor and the shadow fading are applied and a all the data is returned as an channel object Properties name Name of the channel_builder object par The parameter_set object for this channel builder taus The initial delays for each path in s Rows correspond to the MTs columns to the paths pow The normalized initial power squared average amplitude for each path Rows correspond to the MTs columns to the paths The sum over all columns must be 1 AoD The initial azimuth of departure angles for each path in rad AoA The initial azimuth of arrival angles for each path in rad EoD The initial elevation of departure angles for each path in rad EoA The initial elevation of departure angles for each path in rad xpr The initial cross polarization power ratio in dB for each sub path The dimensions correspond to the MT the path number and the sub path number pin The initial phases in rad for each sub path kappa The phase offset angle for the circular XP
10. 1 tx_array rotate_pattern 90 z ja x x de Ra Antenna point skywards l rx_array array rhcp lhcp dipole l rx_array rotate_pattern 90 y axe l visualize view 33 45 lnk 1 tx_position 1l track positions 1 track segment_index 2 l track initial_position hold on plot3 1Ink 1 1nk 2 1nk 3 hold off Generate channel coefficients Next we calculate the channel coefficients p cb 1 create_parameter_sets 0 p 2 scenpar NumClusters 14 p update_parameters c cb get_channels cn c merge 0 2 Parameters Looo00000000000000000000000000000000000000000000000 31 seconds Channels Looo00000000000000000000000000000000000000000000000 15 seconds Merging 00000000000000000000000000000000000000000000000000 2 seconds Copyright Fraunhofer Heinrich Hertz Institute 78 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Tx Position A Tx Antenna O Rx Position WV Rx Antenna Rx Track RIIMOSA_10 45 MIMOS ASAS SANAR IMOSA_10 45_NLOS MIMOSA 10 45_NLOS 200 fe 500 Y Position 300 X Position Figure 18 Scenario setup for the comparison of simulated and measured data Results First we plot the PDP vs distance from the start point see Fig 19 h cn 1 fr 20e6 256 pdp squeeze sum sum abs ifft h 3 2 1 2 pdp 10 logi
11. 45 og O where the og dependency of 4 a comes form the individual angles The function is plotted for different values of L in Fig 11 left and tabularized in Tab 13 Values that are not in the table will be interpolated using linear interpolation The plot in Fig 11 right compares both the values given in og and the values calculated by 44 for the final model including the correction function The parameters for the scatter plot were set to typical values where L ranges from 6 to 30 paths and K ranges from 15 to 15 dB The K Factor has a strong influence on the achievable angular spread E g when the K Factor is 10 then almost 92 of the energy are focussed in one direction Thus it is impossible to get high angular spreads in this case Tab 12 gives an overview of the achievable angular spread in azimuth and elevation direction depending on the K Factor The table is valid for realistic path numbers ranging from 6 to 30 paths Copyright Fraunhofer Heinrich Hertz Institute 64 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Generated Value Unity 2507 N is i T a i T Value of c L K T Angular Spread in Data 507 f i i i i i i g 5 0 5 0 40 60 80 100 120 K Factor db Requested Angular Spread 5 i i 140 160 180 Figure 11 Left Correction function C L K Ind
12. Azimuth angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provides the azimuth sampling angles in radians ranging from 7 to 7 Elevation angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provides the elevation sampling angles in radians ranging from 5 downwards to 5 upwards Enables 1 default or disables 0 the progress bar Output h_array mse mse_pat The QuaDRiGa antenna array object generated from the field patterns The MSE as defined above for each pattern The MSE as defined above for each pattern given for each sample point of the pattern in spherical coordinates Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 28 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE V H CP dist interpolate azimuth elevation element Description Interpolates the field pattern Note Interpolation of the beam patterns is very computing intensive It must be performed several thousands of times during a simulation run It is highly recommended to use linear interpolation for this task since this method is optimized for speed Spline interpolation calls the MATLAB internal interpolation function which is more than 10 times slower To enable linear interpolation set the interpo
13. a copy e User input The user inputs Point 1 in the programm flow are provided through the classes simulation_parameters array track and layout simulation_parameters defines the general settings such as the center frequency and the sample density It also enables and disables certain features of the model such as polarization rotation sub path output and progress bars array combines all functions needed to describe antenna arrays track is used to define user trajectories states and segments layout is a object including the tracks and antenna properties together with further parameters such as the satellite position Internal processing All the processing is done by the classes parameter_set and channel_builder parameter_set is responsible for generating LSPs for the cluster generation It also holds the parameter maps needed for generating auto and crosscorrelation properties of the parameters pa rameter_set implements point 2 of the program flow channel_builder creates the channel coefficients This includes the cluster generation and the MIMO channels It implements steps 3 7 of the program flow e Model output The final two steps 8 and 9 of the program flow are implemented in the class channel Objects of this class hold the data for the channel coefficients The class also implements the channel merger which creates long time
14. 105 fal 110 115 17 5 120 0 5 10 15 20 0 032 0 64 0 96 128 16 1 92 2 24 Distance from start point m Delay us Figure 32 Received power and 2D PDP for the speed profile tutorial Copyright Fraunhofer Heinrich Hertz Institute 100 eMail quadriga hhi fraunhofer de ok WN TE WNE QuaDRiGa v1 2 3 307 A TUTORIALS h ci fr 100 e6 512 h squeeze h pdp 10 logi0 abs ifft h 1 72 figure imagesc pdp 1 256 caxis max max pdp 50 max max pdp 5 J colorbar cm colormap hot colormap cm end 1 1 set gca XTick 1 32 255 set gca XTickLabel 0 32 256 100e6 1e6 xlabel Delay mus set gca YTick 1 ci no_snap 8 ci no_snap set gca YTickLabel O ci no_snap 8 ci no_snap ci no_snap 20 ylabel Time s Now we create a movement profile It is defined by a set of value pairs in track movement_profile The first value represents the time in seconds the second value the position on the track Here we start at a position of 7 m ie in the second NLOS segment We then go back to the beginning of the track This takes 5 seconds Then we wait there for 1 second and go to the end of the track which we reach after additional 14 seconds The next step is to interpolate the sample points This is done by the interpolate movement method It requires the sample interval in s as an input argument Here we choose an interval
15. Alternative approaches CG03 Cla05 are known to have better autocorrelation properties at close distances Le they are better in modeling the distance dependent exponential decay of the correlation However the resulting map is difficult to interpolate since neighboring pixels can have large differences in magnitude We thus extend the WINNER idea by filtering the maps also in the diagonal directions This significantly increases the smoothness of the parameters along a random user trajectory which is an important feature for the time evolution of the channel coefficients The principle of the map based correlation procedure is shown in Fig 8 for an example using the delay spread DS in an urban cellular scenario The granularity of each parameter can be described on three levels the scenario level the link level and the path level Scenario Level The magnitude variance and the correlation of a parameter in a specific scenario e g urban macrocell indoor hotspot or urban satellite are calculated from measurement data Normally parameters are assumed to be log normal distributed For example the median log normal delay spread DS in an urban cellular scenario is 6 89 which corresponds to a DS of a 128 ns see Fig 8 top With a standard deviation of DS 0 5 typical values may lie in between 40 and 407 ns The same principle applies for the other six parameters The decorrelation distance e g DS 40 m describes the distance depende
16. Delay us Distance from start point m 0 032 0 64 0 96 1 28 Delay us y PDP for the linear track without drifting y r r 1 6 1 92 2 24 Figure 30 Received power on the linear track time evolution tutorial 100 105 110 115 120 125 4 130 Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 98 16 17 18 QuaDRiGa v1 2 3 307 A TUTORIALS A 6 Applying Varying Speeds Channel Interpolation One new feature that makes the simulations more realistic is the function to apply arbitrary speed and movement profiles e g accelerating breaking or moving at any chosen speed These profiles are defined in the track class The profiles are then converted into effective sampling points which aid the interpolation of the channel coefficients Channel model setup First we set up the simulation parameters Note the sample density of 2 5 which enables very fast simulations even with drifting simulation_parameters center_frequency 2 53e9 Sample_density 2 5 s s s s use_absolute_delays 1 Second we define a track It has a length of 20 m starts at 10 m east of the transmitter and consists of three segments LOS NLOS LOS The positions are interpolated to match the sample density defined above The track is then plugged into a network layout with one transmitter at position 0 0 25 Both transmitter and receiver
17. Rx Track 150 100F TNES Y Position 100F 150 200 250 0 50 100 150 200 250 300 350 400 450 500 550 X Position Figure 38 Scenario overview manual parameter selection array of two parameter sets The first element p 1 has three positions NLOS segments and the second has one position LOS segment p l create_parameter_sets 1 p 1 name p 1 no_positions p 2 name p 2 no_positions Parameters 00000000000000000000000000000000000000000000000000 5 seconds ans WINNER UMa C2 NLOS_Tx1i ans ans WINNER UMa C2 LOS_Tx1 ans We set the number of clusters for the NLOS segments to 14 Currently it is not possible to have a different number of clusters for each segment i e it is not possible for the first NLOS segment to have 14 clusters and for the second to have only 10 p 1 scenpar NumClusters 14 In order to manually set the parameters we have to overwrite the original settings We do this here for the delay spread The automatically generated values are p 1 ds 1 p 1 ds 2 p 2 ds 1 p 1 ds 3 1e6 ans 0 2696 0 2433 0 0948 0 2094 Copyright Fraunhofer Heinrich Hertz Institute 110 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS We want to set the values of the four segments to 0 45 0 33 0 12 and 0 60 microseconds This is done by p 1 ds 1 0 45e
18. channel_builder get_channels and parameter_set get_sf_profile Now we apply the path gain PG the shadow fading SF and the KF A common PG model for macro cellular settings is given by M Hata Hat80 where the PG scales with the logarithm of the distance d in units of meters between BS and terminal PGrapy A 10 log io dim B 81 A and B are scenario specific coefficients which are typically determined by measurements A often varies between 2 and 4 depending on the propagation conditions the BS height and other factors The values for the SF and the KF are obtained from the map by an interpolation of the surrounding pixels at the position of the st snapshot The KF at the initial position is already included due to the scaling in 34 Thus we have to take this into account and scale the power accordingly Ks raw ies Irt ls pe 5 Ko Irt 1 s for l 1 82 o h otherwise MT 0 1 PG 0 1 SF K P l V10 dB s t Bls a 1 Pi a l 0 Ks and SF qpj are the interpolated values for the KF and the SF from the map Ko is the KF at the initial position PGjqpj is the path gain at the MT position 81 and P is the power of the LOS cluster from 34 Copyright Fraunhofer Heinrich Hertz Institute 71 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Shadow Fading dB Tx Position A Tx Antenna O Rx Segment WZ Rx Antenna Rx Track 100 50 Y
19. fprintf o end mO m1 end al a copy_objects al rotate_pattern a azimuth_grid n 180 pi z for m 1 a no_el a2 al copy_objects a2 rotate_pattern a elevation_grid m 180 pi y cb tx_array a2 c cb get_channels pat m n c coeff end end fprintf 5 0f seconds n round etime clock tStart Copyright Fraunhofer Heinrich Hertz Institute 107 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS 1 Calculating 00000000000000000000000000000000000000000000000000 17 seconds Plot For plotting we use the internal function of the array class We adjust the title of the figures accord ingly 1 d a copy_objects 2 d field_pattern_vertical pat 1 3 d field_pattern_horizontal pat 2 4 x d visualize 6 set x 1 11 String RHCP RHCP 7 set x 1 12 String RHCP LHCP 9 set x 2 11 String LHCP RHCP 10 set x 2 12 String LHCP LHCP 1 close all 2 disp QuaDRiGa Version simulation_parameters version 1 QuaDRiGa Version 1 0 1 145 Element 1 Element 2 RHCP RHCP LHCP RHCP RHCP LHCP LHCP LHCP Attenuation dB Attenuation dB Figure 37 RHCP LHCP antenna patterns Copyright Fraunhofer Heinrich Hertz Institute 108 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS A 9 How t
20. quadriga hhi fraunhofer de ak WN 16 QuaDRiGa v1 2 3 307 A TUTORIALS Tx Position 207 q A Tx Antenna O Rx Position V_ Rx Antenna 15 Rx Track wn L BERLIN UMa NLOS BERLIN UMa LOS pad AFx1 J BERHNOUMB ESN O N UMa NLOS Y Position gt T 5b BERLIN UMa_LOS f 4 A I i i fi i 10 5 0 5 10 15 20 25 30 X Position Figure 28 Scenario setup for the time evolution tutorial RandStream setGlobalStream RandStream mti9937ar seed 2 p 1 create_parameter_sets disp Drifting enabled s drifting_precision 1 RandStream setGlobalStream RandStream mti9937ar seed 1 c p get_channels cn c merge disp Drifting disabled s drifting_precision 0 RandStream setGlobalStream RandStream mti9937ar seed 1 d p get_channels dn d merge Parameters Looo00000000000000000000000000000000000000000000000 J 1 seconds Drifting enabled Channels 00000000000000000000000000000000000000000000000000 J 36 seconds Drifting disabled Channels Looo00000000000000000000000000000000000000000000000 5 seconds Results and discussion Now we plot and discuss the results We start with the power of the LOS tap along the circular track and compare the outcome with and without drifting Fig 29 left degrees 0 cn 1 no_snap 1 cn 1 no_snap 360 los_pwr_drift 10 1log10 squeeze abs cn 1 coe
21. the radiated power towards the Tx becomes minimal at around 90 This minimum is visible in both curves blue and red However the pole of the 45 slanted dipole now points to a different direction which explains the difference in the two lines When the Rx is at 45 and the Tx is vertical the pole is in the right half if the circle resulting in a lower received power When the Rx is Vertical and the Tx is 45 the minimum power is achieved in the left half of the circle Next we evaluate the two dipoles which are rotated by 45 When moving around the circle the Tx stays fixed and the Rx rotates Subsequently at one position we will have both dipoles aligned and at another position both will be crossed When they are crossed the received power will be 0 and when they are aligned the power will match the first plot two vertical dipoles This can be seen in the following figure figure 4 New figure plot abs squeeze c coeff 2 2 1 2 Linewidth 1 axis 0 360 0 1 1 set gca XTick 0 45 360 xlabel Position on circle degrees ylabel LOS Power linear scale title Tz 45 cire Re 45 cire In the last figure we rotated the transmit antenna by 90 It is thus lying on the side and it is horizontally polarized For the Rx we consider three setups Vertical blue line 45 green line and 90 red line Note that the Tx is rotated around the y axis At the initial position 0
22. turns with a probability specified by turn_probability The change of direction is in between 75 and 105 degrees either left or right The radius if the curve is given by curve_radius The track is set up in a way that prevents driving in circles Input track_length the length in m Default length is 1000 m direction specifies the driving direction in rad of the first segment in mathematical sense 0 means east pi 2 means north The default value is random street_length_min the minimal street length in m The default is 50 m street_length_mu the median street length in m The default is 187 m This value was obtained from measurements in Berlin Germany street_length std the standard deviation of the street length in m The default is 83 m This value was obtained from measurements in Berlin Germany curve_radius the curve radius during a turn in m The default is 10 m turn_probability the probability of a turn at a crossing Possible values are in between 0 and 1 The default is 0 5 par generate_parameters overlap usage check_parfiles verbose Description Generates large scale parameters and stores them in par This function extracts the LSPs for the given scenario from the parameter_set class and stores them in track par Hence it automatically generates the LSPs and thus implements an easy to use interface for the parameter_set class Since the t
23. va Le 7 a 4p 1 Ir a ge 1 2p R 3 E rE g 8 lt a A n e 7 I A mT A Br 7 a 0 5 ye i 2f g P 4t q Lo Equal Equal 10 Error 34B f 6 T fi i fi L 9 0 5 1 1 5 30 20 10 0 10 20 30 DS us KF dB P P Figure 17 Comparison of input values and simulation results Copyright Fraunhofer Heinrich Hertz Institute 76 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Next we repeat the same calculation for the K Factor Again we see that the values are almost identical 1 p_nlos sum mean abs coeff 2 end 2 3 2 2 p_los mean abs coeff 1 72 3 3 kf p_los p_nlos 5 figure 6 plot 35 35 35 35 k 7 hold on s plot 35 35 3 35 35 k 9 plot 35 35 3 35 35 k 10 plot 10 logi0 p kf 10 log1i0 kf 11 hold off 12 axis L 30 30 30 30 14 legend Equal 3dB 4 15 xlabel KF_P dB 16 ylabel KF_C dB 17 title K Factor Requested vs generated value Now we repeat the calculation for the RMS delays spread 1 pow_tap abs coeff 2 2 pow_sum sum pow_tap 2 3 mean_delay sum pow_tap delay 2 pow_sum 4 ds sqrt sum pow_tap delay 2 2 pow_sum mean_delay 2 5 ds mean ds 3 figure 8 plot 0 0 1 2 0 0 1 2 k 9 hold on 10 plot 0 0 1 2 1 1
24. 0 0 1 2 k 11 plot 0 0 1 2 0 0 1 2 1 1 k 12 plot p ds 1e6 ds 1le6 13 hold off 14 axis 0 1 5 0 1 5 J 16 legend Equal 10 Error 4 17 xlabel DS_P mus is ylabel DS_C mus 19 title Delay Spread Requested vs generated value The following plot shows the RMSDS of the requested and generated values in dB vs the K factor A value of 3 means that the RMSDS of the generated coefficients is twice a high as in the parameter_set P We see that for a K Factor of up to 30 dB the DS difference is small less than 3 dB 1 figure 2 plot 35 35 0 0 k 3 hold on 4 plot 35 35 3 3 k 5 plot 35 35 3 3 k 6 plot 10 logi0 p kf 10 logi0 ds p ds 7 hold off 8 axis 30 30 6 6 10 legend Equal 3dB 3 11 xlabel KF_P dB 12 ylabel DS_P DS_C dB 13 title Delay Spread difference vs K factor 1 close all 2 disp QuaDRiGa Version simulation_parameters version 1 QuaDRiGa Version 1 0 1 145 Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 77 NNN ONA AR wWONH OO wwwnwnwnwwwwwnn nv nv nvnnn co J YN H O C W e H O e e W N w ye o e w Ne QuaDRiGa v1 2 3 307 A TUTORIALS A 2 Simulating a Measur
25. 1 20 N Number of carriers n Carrier index n 1 N Np Number of receive antennas NE Number of transmit antennas P Cluster power r Receive antenna index t 1 Ny es Delay distribution proportionality factor S Number of snapshots in one segment s Snapshot index within one segment t Transmit antenna index t 1 n v Speed X Y Z Random variables e g X uni Xmin Xmax for uniform or X N u 0o for Normal distri bution Copyright Fraunhofer Heinrich Hertz Institute 7 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 References References 3GP05 3GP11 BHS05 CG03 Cla05 EHBt 13 Gud91 Hats0 HMK JRB KMH 07 MTIO09 NKS 07 NSK 11 OCGD08 PB01 10 14 3GPP TDOC R4 050854 Spatial radio channel models for systems beyond 3G Technical report Elektrobit Nokia Siemens Philips Alcatel Telefonica Lucent Ericsson 8 2005 3GPP TR 25 996 v10 0 0 Spatial channel model for multiple input multiple output MIMO simulations Technical report 3 2011 D S Baum J Hansen and J Salo An interim channel model for beyond 3G systems Proc IEEE VCT 05 Spring 5 3132 3136 2005 Xiaodong Cai and Georgios B Giannakis A two dimensional channel simulation model for shadowing processes EEE Trans Veh Technol 52 6 1558 1567 2003 H Claussen Efficient modelling of channel maps with correlated shadow fading in mobile radio s
26. 11 76 12 12 12 36 12 48 12 84 12 95 13 03 2 5 09 7 66 8 76 9 53 9 98 10 42 10 68 10 99 11 20 11 31 11 45 11 62 11 79 11 87 4 5 33 7 22 8 17 8 67 9 07 9 33 9 53 9 81 9 93 10 01 10 24 10 31 10 44 10 59 6 5 21 664 7 26 7 63 7 94 8 10 8 33 8 51 8 62 8 63 8 82 8 89 9 00 9 08 8 4 80 5 85 6 30 6 60 6 83 6 97 7 12 7 23 7 36 7 38 7 43 7 53 7 62 7 67 10 4 29 5 02 5 38 5 59 5 75 5 87 5 97 6 05 6 11 6 21 6 26 6 30 6 33 6 37 12 3 75 4 28 4 54 4 68 4 79 4 90 4 96 5 01 5 10 5 13 5 19 5 22 5 24 5 27 14 3 20 3 59 3 77 3 90 3 97 4 06 4 11 4 17 4 20 4 23 4 26 4 28 4 32 4 35 16 2 70 2 98 3 11 3 21 3 28 3 33 3 37 3 41 3 44 3 46 3 49 3 52 3 54 3 56 18 2 25 2 46 2 56 2 63 2 68 2 72 2 76 2 78 2 80 2 83 2 85 2 86 2 88 2 89 20 1 86 2 01 2 10 2 14 2 18 2 22 2 24 2 26 2 27 2 29 2 31 2 32 2 33 2 34 Copyright Fraunhofer Heinrich Hertz Institute 65 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 2 3 Drifting of Angles Delays and Phases Implemented in channel_builder get_drifting After cluster delays powers and angles are known for the initial position we update their values for each snapshot of the segment Thus we get an evolution of the parameters over a short time interval Since the values of the LSPs and the number of clusters are kept constant drifting requires that the segment is relatively confined in distance and does not exceed the average autocorrelation distance of the LSPs A similar concept was already in
27. 180 l tx_array field_pattern_vertical A 1 tx_array field_pattern_vertical max max l tx_array set_grid 180 5 180 pi 180 tx_array copy_element 1 2 3 tx_array rotate_pattern 45 y 2 90 Create a new Layout create V polarized dipole 90 10 90 pi 180 Normalize tx_array field_pattern_vertical 90 pi 180 Duplicate the element two more times 45 degree polarization tx_array rotate_pattern 90 y 3 tx_array visualize rx_array 1 tx_array 90 degree polarization Plot the array Use the same array for the Rr PRPPP HEH wee axxo Defining a track The third step defines the track Here we use a circle with 20 m diameter starting in the east traveling north We also choose a LOS scenario since we want to study the LOS polarization The transmitter is located 8 m north of the center of the circle at an elevation of 2 m tx_position 0 12 6 4 Tx position rx_position 20 O O 4 Start position for the Ra track track generate circular 40 pi 0 4 A circular track with radius 10 m track scenario BERLIN_UMa_LOS Chosse the Urban Macro LOS scenario track interpolate_positions s samples_per_meter h Interpolate positions visualize Plot the track PRPPPPH i T t T T T T f T F Tx Position E E EE E E E Tx Antenna O Rx Position Y Rx Antenna J Rx Track 4x1 w T s Y Position c
28. 1e9 r hold off xlabel Distance from track start point ylabel Delay ns The first plot Fig 25 shows the delay of the LOS tap blue and the delay of the first NLOS tap red vs distance The solid lines are from the channel with drifting the dashed lines are from the channel without The LOS delay is always increasing since the Rx is moving away from the Tx However the increase is not linear due to the 25 m height of the Tx Without drifting the delays are not updated and stay constant during the segment The position of the first scatterer is in close distance to the Rx only some m away 190 180 Delay ns a ie ee ee w D nn lon 2 gt S S c N 110 100 20 25 30 35 40 45 50 Distance from track start point Figure 25 Cluster delays vs Rx position drifting phases tutorial Copyright Fraunhofer Heinrich Hertz Institute 91 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS When moving the Rx first approaches the scatterer delay gets a bit smaller and then the distance increases again phase unwrap angle squeeze c coeff 1 1 2 plot distance phase xlabel Distance from track start point ylabel Continuous phase The second plot Fig 26 left shows the phases of the 20 subpaths of the first NLOS tap for the drifting case Note that the phases are not linear This comes fr
29. 2 1 GHz We also want to use drifting in order to get the correct angles for the LOS component and we set the number of transmitters and receivers to one close all clear all set 0 defaultTextFontSize 14 set 0 defaultAxesFontSize 14 s simulation_parameters 4 Set the simulation parameters s center_frequency 2 1e9 4 Center frequency 2 1 GHz s use_polarization_rotation 1 s samples_per_meter 360 40 pi 4 One sample per degree s drifting_precision 1 Setting up the antenna arrays In the second step we set up our antenna arrays We use the synthetic dipole antennas for this case Those antennas show perfect polarization characteristics First we generate a Element 1 Element 2 Element 3 Vertical Vertical Vertical Horizontal Horizontal Horizontal 18 15 12 9 6 3 0 19 16 13 10 7 4 l 19 16 13 10 7 4 l Attenuation dB Attenuation dB Attenuation dB Figure 34 Polarimetric dipole antenna patterns for different orientations Copyright Fraunhofer Heinrich Hertz Institute 103 eMail quadriga hhi fraunhofer de af wn nae wn eB won ee QuaDRiGa v1 2 3 307 A TUTORIALS single dipole with V polarization Then we create multiple copies of this antenna element and rotate them by 45 and 90 degrees respectively We then use the same array for the receiver 1 layout s h 1 tx_array generate dipole l tx_array set_grid 180 10 180 pi
30. 7 and keeping P fixed or adjusting P and keeping 7 fixed In order to avoid this ambiguity an additional proportionality factor delay factor r is introduced to scale the width of the distribution of 7 r is calculated from measurement data See Sec 3 2 1 for more details e Ricean K Factor KF Rician fading occurs when one of the paths typically a line of sight signal is much stronger than Copyright Fraunhofer Heinrich Hertz Institute 49 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE the others The KF K is the ratio between the power in the direct path and the power in the other scattered paths As for the DS the KF is assumed to be log normal distributed The distribution is defined by its median value KF and its STD KF The decorrelation distance KF defines how fast the KF varies when the terminal moves through the environment Angular Spread The angular spread defines the distribution of the departure and arrival angles of each multipath component in 3D space seen by the transmitter and receiver respectively Each path gets assigned an azimuth angle in the horizontal plane and an elevation angle in the vertical plane Thus we have four values for the angular spread 1 Azimuth spread of Departure AsD 2 Azimuth spread of Arrival AsA 3 Elevation spread of Departure EsD 4 Elevation spread of Arrival EsA Each one of them is assumed to be log normal distributed Hence w
31. Description Calculates a power map for the given layout This functions calculates the power seen by a terminal for a given layout This helps to predict the performance and for a given setup Input scenario The scenario for which the map shall be created There are four options 1 A string describing the scenario A list of supported scenarios can be ob tained by calling parameter_set supported_scenarios 2 cell array of strings describing the scenario for each transmitter in the layout 3 A parameter_set object This method is useful if you need to edit the parameters first For example call p parameter_set UMal to load the parameters Then edit p scenpar or p plpar to adjust the settings 4 Aa array of parameter_set objects describing the scenario for each trans mitter in the layout usage A string specifying the detail level The following options are implemented e quick Uses the antenna patterns the LOS path and the path gain from the scenario e sf Uses the antenna patterns the LOS path the path gain from the scenario and a shadow fading map detailed Runs a full simulation for each pixel of the map very slow phase Same as quick but the output contains the complex valued am plitude instead of the power res Distance between sample points in m default 10 m xs x coordinate in m of the top left corner ys y coordinate in m of the top left
32. Forest This is the same as in point 2 The segment on the track gets assigned the scenario Satellite Forest and a third set of maps 15 21 is generated for the Satellite Forest segment The parameters are drawn from those maps new channel coefficients are calculated and the powers of the clusters are ramped up down 12 Turning off without change of environment NLOS Same as in point 4 Copyright Fraunhofer Heinrich Hertz Institute 18 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 Software Structure 2 1 Overview QuaDRiGa is implemented in MATLAB using an object oriented framework The user interface is built upon classes which can be manipulated by the user Each class contains fields to store data and methods to manipulate the data An overview of the class structure is given in Section 2 It is important to keep in mind that all classes in QuaDRiGa are handle classes This significantly reduces memory usage and speeds up the calculations However all MATLAB variable names assigned to QuaDRiGa objects are pointers If you copy a variable i e by assigning b a only the pointer is copied a and b point to the same object in memory If you change the values of b the value of a is changed as well This is somewhat different to the typical MATLAB behavior and might cause errors if not considered properly Copying a QuaDRiGa object can be done by b
33. Milojevic M Landmann G Sommerkorn and R S Thom 3D antenna array model for IST WINNER channel simulations Proc IEEE VTC 07 Spring pages 319 323 2007 M Narandzic C Schneider M K ske S Jaeckel G Sommerkorn and R S Thom Large scale parameters of wideband MIMO channel in urban multi cell scenario Proc EUCAP 11 2011 C Oestges B Clerckx M Guillaud and M Debbah Dual polarized wireless communications From propagation models to system performance evaluation IEEE Trans Wireless Commun 7 10 4019 4031 2008 M F Pop and N C Beaulieu Limitations of sum of sinusoids fading channel simulators IEEE Trans Commun 49 4 699 708 2001 Copyright Fraunhofer Heinrich Hertz Institute 8 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 References PHL 11 J Poutanen K Haneda Lingfeng Liu C Oestges F Tufvesson and P Vainikainen Param eterization of the COST 2100 MIMO channel model in indoor scenarios Proc EUCAP 11 pages 3606 3610 2011 PMF97 K I Pedersen P E Mogensen and B H Fleury Power azimuth spectrum in outdoor environ ments Electronics Letters 33 18 1583 1584 1997 QOHDD10 F Quitin C Oestges F Horlin and P De Doncker A polarized clustered channel model for indoor multiantenna systems at 3 6 GHz IEEE Trans Veh Technol 59 8 3685 3693 2010 Rap02 T S Rappaport Wireless Communications Principles and Practice Prentice Hall 2 edition 2
34. This has been set earlier The last LOS segment has 15 clusters One can see that the three NLOS segment come first followed by the LOS segment The order has been scrambled The following command sorts and combines the segments into one fading sequence c cm merge We now plot the power along the path You can see the power levels of around 102 97 82 and 99 dBm which have been set earlier Each segment has a transition area e g from 2 5m to 5m from 7 5m to 10m and from 12 5m to 15m where the merging took place In those areas the power is scaled linearly That means that for example in between 7 5 and 10m the power ramps up from 97 to 82 dBm power squeeze sum abs c coeff 2 3 power 10 log10 power figure dist t get_length plot dist power title Simulated Power xlabel Distance from start point m ylabel Received Power dBm axis 0 20 110 80 grid on Copyright Fraunhofer Heinrich Hertz Institute 111 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Simulated Power 80 r r r Received Power dBm l Kej wn 105 5 10 15 20 Distance from start point m 110 0 Figure 39 Power along the track manual parameter selection The last plot shows the DS along the path The results reflect the settings of 0 45 0 33 0 12 and 0 60 quiet well As for the power there is an overlap in b
35. XTickLabel 0 32 256 100e6 1e6 xlabel Delay mus set gca YTick 1 cn 1 no_snap 8 cn 1 no_snap set gca YTickLabel 0 cn 1 no_snap 8 cn 1 no_snap cn 1 no_snap 360 ylabel Position on circle degree title PDP for the circular track with drifting The X axis Fig 29 right shows the delay in microseconds and the Y axis shows the position on the circle For easier navigation the position is given in degrees 0 means east starting point 90 means north 180 west and 270 south The LOS delay stays constant since the distance to the Tx is also constant Copyright Fraunhofer Heinrich Hertz Institute 96 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS However the power of the LOS changes according to the scenario Also note that the NLOS segment has significantly more taps due to the longer delay spread Next we create the same plot for the linear track Fig 30 Note the slight increase in the LOS delay and the high similarity of the first two LOS segments due to the correlated LSPs Segment change is at around 6 m h cn 2 fr 100 e6 512 h squeeze h pdp 10 logi0 abs ifft h 1 72 figure imagesc pdp 1 256 caxis max max pdp 50 max max pdp 5 J colorbar cm colormap hot colormap cm end 1 1 set gca XTick 1 32 255 set gca XTickLabel 0 32 256 100e6 1
36. and with a K Factor of 3 dB we get a received power of 0 67W for the LOS component The remaining 0 33W are in the NLOS components The results can be seen in Fig 36 top left New figure Plot the graph Set the azis Add description figure plot abs squeeze c coeff 1 1 72 axis 0 360 0 1 1 xlabel Position degrees ylabel LOS Power linear scale title Tx Vertical Rz Vertical SS NN x Add title disp LOS power num2str mean abs c coeff 1 1 1 72 4 disp NLOS power num2str mean sum abs c coeff 1 1 2 end 2 3 4 LOS power 0 52851 NLOS power 0 22249 The LOS power is almost constant when the Rx is south of the Tx However in close proximity at 90 the power is lowered significantly This comes from the 2 m elevation of the Tx When the Rx is almost under the Tx the radiated power of the Dipole is much smaller compared to the broadside direction The average power of the LOS is thus also lowered to 0 56 W The average sum power if the 7 NLOS components Tx Vertical Rx Vertical Tx Vertical Rx 4 Tx vertical Rx 45 0 97 3 p 0 2 Tx 45 Rx vertical 0 8F 0 8 4 2 2 0 7 4 S S S 2 0 6 8 8 5 05 4 z 04 wn A 0 3 E Q 4 02F 0 1 MN i iA of 0 1 f f A f f f f 0 1 f f f f f f f 50 100 150 200 250 300 35
37. are equipped with dipole antennas The last three lines create the LSPs t track linear 20 0 t initial_position 60 0 1 5 t interpolate_positions 128 20 t segment_index 1 40 90 t scenario WINNER_UMa_C2_L0S WINNER_UMa_C2_NLOS gt WINNER_UMa_C2_LO0S t interpolate_positions s samples_per_meter 1 layout s 1 tx_array generate dipole l rx_array 1 tx_array 1 tx_position 3 25 1 track t 1l visualize RandStream setGlobalStream RandStream mti9937ar seed 5 p 1 create_parameter_sets Parameters 00000000000000000000000000000000000000000000000000 2 seconds Channel generation and results Next we generate the channel coefficients Note that the initial sample density is 2 5 We then interpolate the sample density to 20 It would take ten times as long to achieve the same result with setting the initial sample density to 20 The interpolation is significantly faster It is done by first setting the speed to 1 m s default setting and then creating a distance vector which contains a list of effective sampling points along the track c p get_channels cn c merge t set_speed 1 dist t interpolate_movement s wavelength 2 20 ci cn interpolate dist t get_length spline Channels Looo00000000000000000000000000000000000000000000000 5 seconds The next plot shows the power of the
38. at snapshot s Po nye arctane fr tsy Trt s e CAoD bm 56 Cine arein rrasa Eral crop Gm 57 Sime arctan rrtey Tresct CAoA bm 58 tims arcsin frt s z Ert s CEoA bm 59 We substitute cos Gi m 0 with r i m 0 ro since we are at the Rx position looking towards the Tx Copyright Fraunhofer Heinrich Hertz Institute 67 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION The phases and delays are determined by the length of the vector Cr tm s in Fig 12 bottom This vector points from a random scatterer at the Tx to a random scatterer at the Rx For the sake of simplicity we define the vectors am and bm to be position independent i e their origin is always relative to the Tx and Rx antenna element The vector C m and the path length drt m s then follow from Crtims ba Tr t s Am 60 2 dios F eer 61 Ceti ens Finally the LOS phase w 44 t ar m s and the delay Trt can be calculated using 54 and 55 3 2 4 Geometric Polarization Implemented in channel_builder get_channels and channel_builder generate_xpr As for the angles and the delays the polarization is calculated in a geometric way This is done for each sub path for each snapshot and for each antenna pair r t separately For the sake of simplicity we omit the indices l m s for the path subpath and snapshot in the following Due to the sheer number of c
39. bandwidth where 0 is the begin of the spectrum and 1 is the end For example if a 5 MHz channel should be sampled at 0 2 5 and 5 MHz then carriers must be set to 0 0 5 1 The snapshot numbers for which the frequency response should be calculated By default i e if i_snapshot is not given all snapshots are processed Output freq_response The complex valued channel coefficients for each carrier in frequency domain The indices of the 4 D tensor are Rx Antenna Tx Antenna Carrier Index Snapshot c interpolate dist track_length method Description Interpolates the channel coefficients and delays The channel builder creates one snapshot for each position that is listed in the track object When the channel sampling theorem is not violated i e the sample density is gt 2 then the channel can be interpolated to any other position on the track This can be used e g to emulate arbitrary movements along the track For more information see track movement_profile track interpolate_movement or the tutorial Applying Varying Speeds Channel Interpolation in Section A 6 Input dist A vector containing distance values on the track The distance is measured in m relative to the beginning of the track track_length The total length of the track in m method Selects the interpolation algorithm The default is linear interpolation Optional are
40. c is the speed of light drifting_precision Precision of the drifting functionality drifting_precision 0 This method applies rotating phasors to each path which emulates time varying Doppler characteris tics However the large scale parameters departure and arrival angles shadow fading delays etc are not updated in this case This mode requires the least computing resources and may be preferred when only short linear tracks up to several cm are considered and the distance between transmitter and receiver is large drifting_precision 1 default When drifting is enabled all LSPs are updated for each snapshot This requires significantly more computing resources but also increases the accuracy of the results Drifting is required when e g non linear tracks are generated or the distance between transmitter and receiver is small below 20 m drifting_precision 2 LSPs are updated for each snapshot and for each antenna element at the receiver This increases the accuracy for multi element antenna arrays drifting_precision 3 This option also calculates the shadow fading path loss and K factor for each antenna separately This feature tends to predict higher capacities since is also increases the randomness of the power for different MIMO elements Use with care drifting_update_ threshold Update drifting paths when arrival angle changes degrees Updating the polarization rotation for large antenna arrays is v
41. departure and arrival angels the polarization the shadow fading and the K Factor based on the position of the terminal Scenario transitions When the mobile terminal MT moves through the fading channel it may pass through several different scenarios QuaDRiGa supports smooth transitions between adjacent channel segments This is used to emulate long term time evolution and allows the simulation of e g handover scenarios Variable speeds for mobile terminals QuaDRiGa supports variable speeds including accelerating and sowing down of mobile terminals Common framework for LOS and NLOS simulations In WINNER line of sight LOS and non line of sight NLOS scenarios were treated differently QuaDRiGa used the same method for both scenarios types This reduces the model complexity and enables freely configurable multicell scenarios E g one MT can see two base stations BSs one in LOS and another in NLOS Geometric polarization The polarizations for the LOS and for the NLOS case is now calculated based on a ray geometric approach Improved method for calculating correlated large scale parameters LSPs The WINNER model calculates maps of correlated parameter values using filtered random numbers QuaDRiGa uses the same method but extends the map generation algorithm to also consider diagonal movement directions and to create smoother outputs New functions for modifying antenna patterns Antenna patterns can now be freely rotated in 3
42. e g 307 ns for segment 1 152 ns for segment 2 233 ns for segment 3 and so on This is done for all LSPs 3 The trajectory describes the position of the MT in the maps For each segment of the trajectory clusters are calculated according to the values of the LSPs at the map position The cluster positions are random within the limits given by the LSP For example a delay spread of 152 ns limits the distance between the clusters and the terminal 4 Each cluster is split into 20 sub paths and the arrival angles are calculated for each sub path and for each positions of the terminal on the trajectory Copyright Fraunhofer Heinrich Hertz Institute 14 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW The antenna response for each of the arrival angels is calculated the same holds for the departure angles If there is more than one antenna at the transmitter and or receiver side the calculation is repeated for each antenna The phases are calculated based on the position of the terminal antennas in relation to the clusters The terminal trajectory defines how the phases change This results in the Doppler spread The coefficients of the 20 sup paths are summed the output are paths If there is more than one antenna and depending on the phase this sum results in a different received power for each antenna pair At this point the MIMO channel response is created The channel
43. e linear Linear interpolation optimized for speed e spline Cubic spline interpolation of the channel coefficients and piecewise cubic hermite polynomial interpolation for the delays Output c A channel object containing the interpolated coefficients and delays for each entry in dist Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 45 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE c merge overlap optimize verbose Description Combines channel segments into a continuous time evolution channel The channel merger implements the continuous time evolution with smooth transitions between seg ments Each segment of a track is split in two parts an overlapping area with the previous segment and an exclusive part with no overlapping Each segment is generated independently by the channel builder However the distance dependent autocorrelation of the large scale parameters was considered when the parameters were drawn from the corresponding statistics Transition from segment to segment is carried out by replacing taps of the previous segment by the taps of the current segment one by one The modeling of the birth death process is done as published in the documentation of the WIM2 channel model The route between adjacent channel segments is split into sub intervals equal to the minimum number of taps in both overlapping segments During each sub interval the power of one old
44. each parameter can be different We account for the cross correlation of the parameters by assembling a matrix X that contains all cross correlation values and multiplying its matrix square root with the values of the seven parameter maps Bpg Bzsp The cross correlation matrix needs to be positive definite to get a unique real numbered solution 5 By x Ds y 2 DS X 30 By 2 BsD Be ses Last the users are placed on the maps and the corresponding values for the LSPs are obtained In this way we initialize the second part of the channel model the generator of the individual channel coefficients with correlated input values for each user An example output of the correlation map procedure is shown in Fig 10 A set of 200 mobile terminals is randomly placed in an urban cellular scenario blue dots The transmitter is situated in the scenario center red cross The map for the DS is displayed as a background image The initial values of the LSPs for each user position link level serve as an input for calculating the channel coefficients They are simply generated by reading the map values around the position of the mobile terminal and interpolating between adjacent pixels RMS Delay Spread ns 200 250 E a Tx Position A Tx Antenna O Rx Segment WZ Rx Antenna Y Position 200 150 100 50 0 50 100 150 200 X Position Figure 10 Illustration of the map based generation of the DS Copyright Fraun
45. fruitful discussions on the QuaDRiGa channel model and the manuscript of this document How to Cite this Document JRB 14 S Jaeckel L Raschkowski K Borner L Thiele F Burkhardt and E Eberlein QuaDRiGa Quasi Deterministic Radio Channel Generator User Manual and Documentation Fraun hofer Heinrich Hertz Institute Tech Rep v1 2 3 307 2014 Copyright Fraunhofer Heinrich Hertz Institute 2 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 Contents Contents 1 Introduction and Overview 10 1 1 Installation and System Requirements 0 0 02 eee ee 10 1 2 General Remarks 1 rik d KOKE Oh oa ao a ebe ai a a ea 10 i3 Introduction to Qua DRIGA pocs e oaos ee goa Pe a R ee GOR a we ee 11 la Coat ines Me Gyu HOn o ip e soa ee acin E BO ed ee ee i ee aa a ae 13 Lo Qualities Frodon PION o eo soe cete es ee eh ee gug e a d a eee Gee e 14 1 6 Description of modeling of different reception conditions by means of a typical drive course 15 2 Software Structure 19 ul Qyvernview cometas mas acarpa Epa a aa poenae ao See RO ee 19 2 2 Description of Classes Properties and Methods 020020202 a 21 221 Clis simula Parameters o suos oa eop aa a a e a E a E a oe te 23 Uae Cha aia esa eee hee ch ea HBS Pe e e eee Bee ee paa dy os 25 ee Obas rack he ee ee a a ee EE oS ee ae df Bes 30 Jad Class IANO awe bk ra ae Pewee en Ee oe SEA Ee eee Les 35 22 0 Clase parameterset soc cb ce eed eG De ee e
46. having a specific beam pattern that can be combined in any geometric arrangement A set of synthetic arrays that allow simulations without providing your own antenna patterns is provided see generate method for more details Properties name Name of the antenna array interpolation_ Method for interpolating the beam patterns method The default is linear interpolation Optional are e nearest Nearest neighbor interpolation QuaDRiGa optimized e linear Linear interpolation QuaDRiGa optimized Default e spline Cubic spline interpolation MATLAB internal function e nearest_int Nearest neighbor interpolation MATLAB internal function e linear_int Linear interpolation MATLAB internal function Note MATLAB internal routines slow down the simulations significantly no_elements Number of antenna elements in the array Increasing the number of elements creates new elements which are initialized as copies of the first element Decreasing the number of elements deletes the last elements from the array elevation_grid Elevation angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provides the elevation sampling angles in radians ranging from 4 downwards to upwards azimuth_grid Azimuth angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provide
47. in the config files They get converted into a structure parameter_set scenpar Parameter Unit or type Description plpar model Text string Selects the model for average path loss For satellite applications the pathloss is PL_model defined by the satellite distance and is assumed to be constant for the reception are For terrestrial cases pathloss models like Hata or others e g WINNER pathloss models can be selected plpar A dB For satellite applications this parameter defines the average path loss and is equiv PL_A alent to the mu of the lognormal distribution of the shadow fading Parameters in structure parameter_set scenpar Large Scale Parameters Those parameters describe how the large scale parameters vary within a propaga tion environment SF_sigma dB Those parameter describe the slow fading implemented as Lognormal distribu SF_lambda meter tion and filtered see map generation according to the de correlation distance lambda KF_mu dB Statistical properties of the K factor KF sigma dB KF_lambda meter Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 50 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE xpr_mu dB The XPR is defined by the XPR Antenna see antenna pattern and the XPR xpr_sigma dB environment The parameter
48. of the environment but distributes the positions of the scattering clusters the sources of indirect signals such as buildings or trees randomly A simplified overview of the model is depicted in Figure 2 For each path the model derives the angle of departure the angle between the transmitter and the scattering cluster the angle of arrival the angle between the receiver and the scattering cluster and the total path length which results in a delay 7 of the signal For the sake of simplicity only two paths are shown in the figure Scatterer OA NLOS NLOS PA o F LOS Path il th d LOS A A aT length ERO Oe Rscenree Ig LOS Transmitter Power Impulse Response Signal LOS NLOS delay Figure 1 Simplified overview of the modeling approach used in QuaDRiGa Terrestrial and Satellite scenarios can be modeled For Satellite to Earth communication the angle of departure is identical for all clusters The concept behind the model allows also the modeling of scenarios such as e Earth to satellite e Satellite systems with complementary ground components CGC Using several transmitters at dif ferent positions and simulating all propagation paths in one setup is supported The analysis of these scenarios was not in the scope of the MIMO over Satellite MIMOSA project This feature is not tested and especially no parameter sets are available yet In the following the terms cluster scattering cluster and scatterer are
49. parameters This variable contains structure of the LSPs with the following fields ds The delay spread in s per segment kf The Ricean K Factor in dB per snapshot pg The effective path gain in dB excluding antenna gains per snapshot asD The azimuth angle spread in deg per segment at the transmitter asA The azimuth angle spread in deg per segment at the receiver esD The elevation angle spread in deg per segment at the transmitter esA The elevation angle spread in deg per segment at the receiver xpr The NLOS cross polarization in dB per segment An identical copy of this variable is assigned to track par Copyright Fraunhofer Heinrich Hertz Institute 32 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE len dist get_length Description Calculates the length of the track in m Output len Length of a track in m dist Distance of each position snapshot from the start of the track in m subtracks get subtrack i segment Description Splits the track in subtracks for each segment state This function returns the subtracks for the given segment indices When no input argument is provided all subtracks are returned After defining segments along the track one needs the subtrack that corresponds only to one segment to perform the channel calculation This new track can consist of two segments The first segm
50. power delay profile This parameter enables an additional variation of the individual cluster powers around the PDP r_DS This parameter allows the mapping of delay spreads to delays and powers for the clusters See section 3 2 1 Copyright Fraunhofer Heinrich Hertz Institute 51 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 4 2 Adding New Scenarios The scenario parameters are set in parameter_set scenpar Here you also have the option to change individual parameters by assigning new values The scenario UMal for example uses by default 8 clusters When the simulations should be done with 15 clusters one can change the settings by N p parameter_set BERLIN_UMa_LOS 4 New parameter set p scenpar NumClusters 15 4 Set new number of clusters A list of currently supported scenarios is generated by parameter_set supported_scenarios The default settings of those scenarios are stored in config files which are located in the config folder of the QuaDRiGa source path The UMal config file for example looks like this rar won Nan A Pe Fe FR RRP RP wWwwWwwWwHwWwwNwnwnnwnwnn nNnnnNNNN ND WD PFPOwM MANA TAA AHONHH OCH ANAAKEWNHHO OC or W N Q w N aana n S Config File for scenario UMal WINNER Urban Macro LOS See CELTIC CP5 026 D5 3 WINNER Final Channel Models and IST 4 027756 WINNER II D1 1 2 v 1 1 WINNER II Chann
51. sampling rate in advance as the max speed is fixed as well To overcome this problem it is advantageous to sample the channel in the spatial domain rather than in the time domain f gt 4 4 max v C Here fs denotes the spatial sampling rate in samples per meter In its normalized form it is also known as sample density SD fs SD 22 5 T Another advantage of sampling in the spatial domain is the reduction of required storage since the number of samples is reduced in case the maximum speed is greater than 1 meter per second 3 6 kilometers per hour Copyright Fraunhofer Heinrich Hertz Institute 55 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 1 3 The Antenna Model One feature of QuaDRiGa is that it allows the separations of propagation and antenna effects An antenna is defined by its directional response F also known as field pattern The original SCM considers only 2D propagation Thus F is a function of the azimuth angle which is also indicated in Fig 2 Later extensions SZM 06 also consider the elevation direction 0 The complex amplitude g of one tap between a transmit antenna t and a receive antenna r notes gri VP F 69 6 M F 44 6 eI 54 6 where F and F are the patterns at the receiver and transmitter respectively A is the wavelength and P is the power of the tap Note that the patterns F 6 are two element vectors which contain the v
52. tap ramps down and one new tap ramps up Power ramps are modeled by a modified sinus function to allow smooth transitions Taps from the old and new segments are coupled based on their power If the number of clusters is dif ferent in the channel segments the weakest clusters are ramped up or down without a counterpart from the new old segment The merging is only done for the NLOS components since the LOS component has a deterministic behavior The LOS component is thus just scaled in power Input overlap The length of the overlapping part relative to the segment length It can have values in between 0 no overlap and 1 ramp along the entire segment A value of 0 disables the merging process and the channel segments are simply concatenated A value of 1 constantly merges the channels The default setting is 0 5 optimize The channel merger tries to automatically optimize the pairing of the taps i e one tap if the old segment ramps down and one of the new ramps up This is enabled by default but it is computing intensive For quicker results it can be disabled by setting optimize to 0 verbose Enables 1 default or disables 0 the progress bar Output c An array of channel objects containing the merged coefficients and delays Copyright Fraunhofer Heinrich Hertz Institute 46 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 3 Data Flow The data flow of the QuaDR
53. the channel parameters are calculated from the distributions Specific channel realizations are generated by summing contributions of rays with specific channel parame ters like delay power angle of arrival and angle of departure Different scenarios are modeled by using the same approach but different parameters The basic features of the model approach can be summarized as follows e Support of freely configurable network layouts with multiple transmitters and receivers Scalability from a single input single output SISO or multiple input multiple output MIMO link to a multi link MIMO scenario Same modeling approach indoor outdoor and satellite environments as well as combinations of them Support of a frequency range of 2 6 GHz with up to 100 MHz RF bandwidth Support of multi antenna technologies polarization multi user multi cell and multi hop networks Smooth time evolution of large scale and small scale channel parameters including the transition be tween different scenarios e High accuracy for the calculation of the polarization characteristics Copyright Fraunhofer Heinrich Hertz Institute 10 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW The QuaDRiGa channel model largely extends the WINNER model to support several new features that were originally not included These are Time evolution Short term time evolution of the channel coefficients is realized by updating the delays the
54. the signals that are fed to the antenna elements For example in order to transmit a left hand circular polarized LHCP signal two antenna elements are needed They are then coupled by a matrix al V2 5 The rows in the matrix correspond to the antenna elements the columns to the signal ports In this example the antenna has one port i e it is fed with one input signal This signal is then split into two and fed to the two antenna elements where the second element radiates the signal with 90 phase shift In a similiar fasion it is possible to create fixed beamforming antennas and include crosstalk between antenna elements By default coupling is set to an identity matrix which indicates perfect isolation between the antenna elements no_az Number of azimuth values no _el Number of elevation values Copyright Fraunhofer Heinrich Hertz Institute 25 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE Methods h_array array array_type phi_3dB theta_3dB rear_gain Description Creates a new array object See array generate for a description of the input parameters and the list of supported antenna types apply_common_phase element Description Applies the common phase to the patterns Input element The element numbers for which this functions is applied If no element number is given the common phase is applied to all ele
55. the source of a reflected or scattered wave arriving at the receiver Typically there are less clusters in a LOS scenario then in a NLOS scenario Note that the number of clusters directly influences the time needed to calculate the coefficients e Path Loss PL A common path loss PL model for cellular systems is the log distance model where A and B are scenario specific coefficients The path loss exponent A typically varies between 2 and 4 depending on the propagation conditions the base station height and other influences They are typically determined by measurements d is the distance in units of meters between the transmitter and the receiver Note that in Tab a factor of 10 is applied to the PL exponent In other environments such as in satellite systems the PL does not depend on the distance but has a constant value In this case A would be 0 e Shadow Fading SF Shadow fading occurs when an obstacle gets positioned between the wireless device and the signal transmitter This interference causes significant reduction in signal strength because the wave is shadowed or blocked by the obstacle It is modeled as log normal distributed with two parameters The standard deviation o defines the width of the distribution i e the power value in dB above or below the distance dependent PL and the decorrelation distance A This parameter defines how fast the SF varies when the terminal moves through the environment E g a value of 87
56. version QuaDRiGa Version 1 0 1 145 20 80 i 25 9 16 85 90 g E 12 TEE T3 95 3 10 4 g 10 100 5 E Z 8 12 5 a E 6f 15l 110 ji 115 al 17 5 120 5 10 15 20 0 0 32 0 64 0 96 128 1 6 1 92 2 24 Time s Delay us Figure 33 Movement profile left and interpolated PDP right Copyright Fraunhofer Heinrich Hertz Institute 102 eMail quadriga hhi fraunhofer de a PF ON QuaDRiGa v1 2 3 307 A TUTORIALS A 7 Geometric Polarization Here we demonstrate the polarization rotation model that calculates the path power for polarized antenna arrays We do this by setting up the simulation with different H V polarized antennas at the transmitter and at the receiver Then we define a circular track around the receiver When the receiver moves around the transmitter it changes its antenna orientation according to the movement direction In this way all possible departure and elevation angles are sampled Depending on the antenna orientation the polarizations are either aligned e g the Tx is V polarized and the Rx is V polarized they are crossed e g the Tx is V polarized and the Rx is H polarized or the polarization orientation is in between those two The generated channel coefficients should reflect this behavior Setting up the simulation environment First we have to set up the simulator with some default settings Here we choose a center frequency of
57. 0 0 50 100 150 200 250 300 350 Position degrees Position degrees Tx 45 Rx 45 Tx 90 Rx 0 45 90 0 9 0 9 ape i Rx 45 0 8 0 8 Rx 90 2 2 07 4 S S S Z 0 6 8 8 5 05 3 3 0 4 A a 7 7 0 3 0 2 0 1 0 la 0 1 1 Ll 1 Ll 1 1 1 0 1 1 1 1 1 1 1 1 0 45 90 135 180 225 270 315 360 0 50 100 150 200 250 300 350 Position on circle degrees Position degrees Figure 36 Results from the geometric polarization tutorial Copyright Fraunhofer Heinrich Hertz Institute 105 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS is 0 26 W This mainly come from the XPR which leakes some power from the vertical into the horizontal polarization and thus reduces the received power on the vertically polarized Dipole Next we study two cases Either the Tx is vertical polarized and the Rx is at 45 or vise versa Fig 36 top right figure 4 New figure plot abs squeeze c coeff 2 1 1 72 4 Tx vertical Ra 45 degree hold on plot abs squeeze c coeff 1 2 1 2 r 4 Tx 45 degree Ra vertical hold off axis 0 360 0 1 1 legend Tx vertical Rx 45 circ Tx 45 circ Rx vertical xlabel Position degrees ylabel LOS Power linear scale title Tx Vertical Rx 46 cire The receiver changes its direction in a way that it always has the same orientation towards the Tx However due to the displacement of the Tx
58. 0 35 40 45 50 20 25 30 35 40 45 Distance from track start point Distance from track start point Figure 27 Phases and Tx power vs Rx position without drifting drifting phases tutorial 50 Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 93 QuaDRiGa v1 2 3 307 A TUTORIALS A 5 Time Evolution and Scenario Transitions The channel model generates the coefficients separately for each segment In order to get a time continuous output these coefficients have to be combined This is a feature originally described in the documentation of the WIM2 channel model although it was never implemented Since this component is needed for time continuous simulations it was implemented here This script sets up the simulation and creates such time continuous CIRs Channel model setup and coefficient generation First we set up the channel model close all clear all set 0 defaultTextFontSize 14 set 0 defaultAxesFontSize 14 simulation_parameters center_frequency 2 53e9 Sample_density 4 use_absolute_delays 1 nnn DN Second we create a more complex network layout featuring an elevated transmitter 25 m and two receivers at 1 5 m height The first Rx moves along a circular track around the receiver The second receiver moves away from the Tx Both start at the same point Note that each track is split into three segments The first Rx goes from an LOS area to a
59. 0 pdp figure imagesc pdp end 1 1 1 192 cm colormap hot colormap cm end 1 1 caxis max max pdp 60 max max pdp 5 J colorbar title Time variant power delay profile set gca XTick 1 32 192 set gca XTickLabel 0 32 192 20e6 1e6 xlabel Delay mus ind sort cn no_snap cn 1 no_snap 10 1 set gca YTick ind set gca YTickLabel round sort 500 ind 3 descend ylabel Distance m Time variant power delay profile Distance m 0 1 6 3 2 4 8 6 4 8 Delay us Figure 19 2D PDP of the simulated track Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 79 16 7 18 19 20 9 10 11 13 14 10 QuaDRiGa v1 2 3 307 A TUTORIALS The next plot shows the total received power along the path Fig 20 top left Green shaded ares are LOS The rest is NLOS dist 1 cn no_snap l track get_length cn no_snap ind find strcmp l track scenario S1 los for n 1 numel ind los los 1 track segment_index ind n 1 track segment_index ind n 1 end power 10 1logi0 sum reshape abs cn coeff 2 cn no_snap 1 4 ar zeros 1 cn no_snap ar los 200 figure a area dist ar set a i FaceColor 0 7 0 9 0 7 set a LineStyle none hold on plot dist power hold off title Posi
60. 002 SNKt 10 C Schneider M Narandzic M Kaske G Sommerkorn and R S Thoma Large scale param eter for the WINNER II channel model at 2 53 GHz in urban macro cell Proc IEEE VTC 10 Spring 2010 Sva01 T Svantesson A physical MIMO radio channel model for multi element multi polarized an tenna systems Proc IEEE VTC 01 Fall 2 1083 1087 2001 SZM 06 M Shafi Min Zhang A L Moustakas P J Smith A F Molisch F Tufvesson and S H Simon Polarized MIMO channels in 3 D models measurements and mutual information FEE J Sel Areas Commun 24 514 527 Mar 2006 ZRP 05 Y Zhou S Rondineau D Popovic A Sayeed and Z Popovic Virtual channel space time processing with dual polarization discrete lens antenna arrays IEEE Trans Antennas Propag 53 2444 2455 Aug 2005 Copyright Fraunhofer Heinrich Hertz Institute 9 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW 1 Introduction and Overview 1 1 Installation and System Requirements The installation is straightforward and it does not require any changes to your system settings If you would like to use QuaDRiGa just extract the ZIP File containing the model files and add the source folder from the extracted archive to you MATLAB Path This can be done by opening MATLAB and selecting File Set Path from the menu The you can use the Add folder button to add QuaDRiGa to your MATLAB
61. 1 end los los start stop end power reshape 10 logi0 squeeze sum abs cn coeff 2 3 4 1 mi min reshape power 1 5 ma max reshape power 1 5 ar ones 1 cn no_snap ma ar los mi figure Position 100 100 1000 700 a area dist ar set a i FaceColor 0 7 0 9 0 7 set a LineStyle none hold on plot dist power hold off Copyright Fraunhofer Heinrich Hertz Institute 87 eMail quadriga hhi fraunhofer de 40 e WNE 10 16 QuaDRiGa v1 2 3 307 A TUTORIALS xlabel Track m ylabel Received Power per MIMO LINK dB axis 0 t get_length mi ma legend L0S tP tits Ria PUe eit P Teer 4 box on title Received power along the track The next plot shows the RMS delay spread along the path for the first MIMO element Again shaded ares are for the LOS segments pow_tap abs squeeze cn coeff 1 1 72 pow_sum sum pow_tap 1 mean_delay sum pow_tap cn delay 1 pow_sum ds sqrt sum pow_tap cn delay 2 1 pow_sum mean_delay 2 ar zeros 1 cn no_snap ar los 10000 figure Position 100 100 1000 700 a area dist ar set a i FaceColor 0 7 0 9 0 7 set a LineStyle none hold on plot dist ds 1le9 hold off ma 1e9 max ds 0 1 max ds axis 0 t get_length O ma xlabel T
62. 6 p 1 ds 2 0 33e 6 p 2 ds 1 0 12e 6 p 1 ds 3 0 60e 6 The K Factor and the shadow fading are read from the map during the generation of channel coefficients This would overwrite any manual values However this could be deactivated A drawback is that in this case the KF SF and PL are only updated once per segment This will result in a step like function of the output power There is currently no method the set the power manually on a per snapshot basis In the following example we want to fix the received power to 102 97 82 and 99 dBm K Factors are taken from the map p 1 plpar 4 Disable path loss for NLOS p 2 plpar 4 Disable path loss for LOS p 1 sf 10 7 0 1 102 97 99 4 Set power for NLOS segments p 2 sf 10 0 1 82 4 Set power for LOS segments p 1 map_valid false 4 Disable automatic overwrite for NLOS p 2 map_valid false 4 Disable automatic overwrite for LOS Calculate channel coefficients Now we calculate the coefficients and the received power along the path The following command calculate the channel coefficients We then check the number of clusters that have been produced for each segment cm p get_channels 4 Calculate the channel coefficients cat 1 cm no_path 4 Display the number of paths Channels Looo00000000000000000000000000000000000000000000000 7 seconds ans 14 14 14 8 The first three segments have 14 clusters
63. 7 a 20 15 10 5 0 5 10 15 20 X Position Figure 35 Scenario layout Generating channel coefficients Now we have finished the parametrization of the simulation and we can generate the parameters We thus create a new set of correlated LSPs and fix the shadow fading and the K factor to some default values This disables the drifting for those parameters We need to do that since otherwise drifting and polarization would interfere with each other RandStream setGlobalStream RandStream mti9937ar seed 1 p 1 create_parameter_sets Create parameter sets p parameter_maps 2 3 4 Fis KF to 3 dB p parameter_maps 3 0 4 Fix SF to 0 dB p plpar 4 Disable path loss model p update_parameters c cb p get_channels 4 Get the channel coefficients Parameters 00000000000000000000000000000000000000000000000000 1 seconds Channels Looo00000000000000000000000000000000000000000000000 3 seconds Copyright Fraunhofer Heinrich Hertz Institute 104 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Results and Evaluation We now check the results and confirm if they are plausible or not We start with the two vertically polarized dipoles at the Tx and at the Rx side The model creates 8 taps which is the default for the Urban Macro LOS scenario Without path loss and shadow fading SF 1 the power is normalized such that the sum over all taps is 1W
64. CP response like in a measurement in an anechoic chamber Set up the array We set up a patch antenna with an opening angle of 120 We then copy that patch and rotate it by 90 around the x axis to create an X Pol array We then set the coupling to 90 phase to transmit circular polarized waves resolution 10 4 in Degrees w array custom 120 120 0 set_grid 180 resolution 180 pi 180 90 resolution 90 pi 180 a copy_element 1 2 w b a copy_objects a rotate_pattern 90 x 2 a coupling 1i sqrt 2 1 1 1j 1j b rotate_pattern 90 x 2 b coupling 1 sqrt 2 1 13 1j 1j Place arrays in layout We place two of those arrays in a layout The scenario LOSonly has no NLOS scattering One can see this setup as a perfect anechoic chamber 1 layout 1 simpar show_progress_bars 0 1 simpar drifting_precision 0 rx_position 11 0 0 track no_snapshots 1 track ground_direction pi track scenario LOSonly sta array a rx_array b PRRPRPHH p l create_parameter_sets cb p get_channels cb pin zeros size cb pin Get array response We now sample the array response for each degree in the antenna array pat zeros a no_el a no_az 2 2 values a no_az fprintf Calculating mO 0 tStart clock 4 A Status message for n 1l a no_az mi ceil n values 50 if mi gt mO for m2 1 m1i m0
65. Copyright Fraunhofer Heinrich Hertz Institute 90 eMail quadriga hhi fraunhofer de woe o e QuaDRiGa v1 2 3 307 A TUTORIALS RandStream setGlobalStream RandStream mti9937ar seed 5 p l1 create_parameter_sets p parameter_maps 2 3 0 4 Fix SF and KF to 0 p plpar 4 Disable path loss model p update_parameters Parameters 00000000000000000000000000000000000000000000000000 1 seconds Now we generate the channel coefficients The first run uses the new drifting module the second run disables it Note that drifting needs significantly more computing resources In some scenarios it might thus be useful to disable the feature to get quicker simulation results s drifting_precision 1 RandStream setGlobalStream RandStream mti9937ar seed 2 c p get_channels s drifting_precision 0 RandStream setGlobalStream RandStream mti9937ar seed 2 d p get_channels Channels Looc00000000000000000000000000000000000000000000000 12 seconds Channels 00000000000000000000000000000000000000000000000000 J 7 seconds Results and discussion The following plots represent the results of the test figure distance 20 1 c no_snap l track get_length c no_snap plot distance c delay 1 1e9 b hold on plot distance d delay 1 1e9 b plot distance c delay 2 1e9 r plot distance d delay 2
66. D coordinates while maintaining the polarization properties New MATLAB implementation The MATLAB code was completely rewritten The implementations now fosters object oriented programming and object handles This increases the performance significantly and lowers the memory usage 1 3 Introduction to QuaDRiGa QuaDRiGa QUAsi Deterministic RadIo channel GenerAtor was developed to enable the modeling of MIMO radio channels for specific network configurations such as indoor satellite or heterogeneous configurations Besides being a fully fledged three dimensional geometry based stochastic channel model QuaDRiGa con tains a collection of features created in spatial channel model SCM and WINNER channel models along with novel modeling approaches which provide features to enable quasi deterministic multi link tracking of users receiver movements in changing environments The main features of QuaDRiGa are Three dimensional propagation antenna modeling geometric polarization scattering clusters Continuous time evolution Spatially correlated propagation parameter maps Transitions between varying propagation scenarios Copyright Fraunhofer Heinrich Hertz Institute 11 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW The QuaDRiGa approach can be understood as a statistical ray tracing model Unlike the classical ray tracing approach it doesn t use an exact geometric representation
67. Path Table 1 QuaDRiGa System Requirements Requirement Value Minimal required MATLAB version 7 12 R2011a Required toolboxes none Memory RAM requirement 1 GB Processing power 1 GHz Single Core Storage 50 MB Operating System Linux Windows Mac OS 1 2 General Remarks This document gives a detailed overview of the QuaDRiGa channel model and its implementation details The model has been evolved from the Wireless World Initiative for New Radio WINNER channel model described in WINNER II deliverable D1 1 2 v 1 1 KMHT07 This document covers only the model itself Measurement campaigns covering the extraction of suitable parameters can be found in the WINNER documentation KMH 07 HMK 10 or other publications such as SNK 10 NSK 11 Furthermore the MIMOSA project EHB 13 covers the model development and parameter extraction for land mobile satellite channels The QuaDRiGa channel model follows a geometry based stochastic channel modeling approach which allows the creation of an arbitrary double directional radio channel The channel model is antenna independent i e different antenna configurations and different element patterns can be inserted The channel parame ters are determined stochastically based on statistical distributions extracted from channel measurements The distributions are defined for e g delay spread delay values angle spread shadow fading and cross polarization ratio For each channel segment
68. Path from KMH t07 oaaae eee eee 64 Maximum Linear Angular Spread vs K Factor 00 a 65 Values of Gg te R xo edo rad ee a Se ERS SH oe A Re Se Eee ESS ES Ce 65 List of Acronyms AoA AoD BS CIR DS FIR KF LBS LHCP LOS LSP MIMO MPC MSE MT NLOS PDP PG PL RHCP Rx SCM SF SISO SSG Tx UML angle of arrival 54 68 angle of departure 54 base station 11 14 35 38 54 55 71 channel impulse response 23 33 42 60 62 94 delay spread 14 59 62 72 73 finite impulse response 60 Ricean K factor 59 62 63 71 last bounce scatterer 66 67 left hand circular polarized 25 27 58 line of sight 11 13 23 30 37 38 42 43 48 51 54 55 63 66 68 70 72 80 81 83 86 88 91 94 97 99 100 103 105 109 111 large scale parameter 11 13 14 16 17 23 32 36 38 39 41 44 48 59 61 66 72 74 90 94 97 99 104 109 multiple input multiple output 10 11 13 15 23 38 44 56 MIMOSA MIMO over Satellite 12 multipath component 12 54 71 mean square error 28 mobile terminal 11 13 42 44 54 55 71 72 non line of sight 11 18 23 42 49 50 62 66 69 70 80 81 83 85 87 91 92 94 97 99 101 105 107 109 111 power delay profile 62 path gain 32 36 71 path loss 49 right hand circular polarized 27 58 receiver 63 66 68 71 spatial channel model 11 56 66 69 shadow fading 71 single input single output 10 state sequence generat
69. Position 100 150 200 250 gt _ gt 200 150 100 50 0 50 100 X Position Figure 14 Illustration of the SF drifting along a terminal track 3 2 7 Transitions between Segments Until now the calculations were done for each segment of a MT trajectory independently Longer sequences are created by merging the channels from adjacent segments into one long sequence The basic idea comes from the documentation of the WINNER II model KMH 07 However it was neither implemented nor tested Our implementation requires overlapping segments as depicted in Fig 15 top Like in a movie the transition is carried out by ramping down the power of paths in the old segment and at the same time ramping up the power of paths from the new segment Hence this process describes the birth and death of clusters along the trajectory In order to keep the computational overhead low we split the overlapping part into several sub intervals equal to the minimum number of clusters in both segments During each sub interval the power of one old cluster ramps down and one new cluster ramps up We model power ramps by a squared sine function to allow smooth transitions wel sin 7 wits 83 wlin is a linear ramping function ranging from 0 to 1 wb l is the corresponding sine shaped ramping function having the advantage of a constant slope at the points 0 and 1 which prevents inconsistencies at the edges of the intervals I
70. Quasi Deterministic Radio Channel Generator User Manual and Documentation Aa Quadriga The Next Generation Radio Channel Model Document Revision v1 2 3 307 April 30 2014 Fraunhofer Heinrich Hertz Institute Wireless Communication and Networks Einsteinufer 37 10587 Berlin Germany e mail quadriga hhi fraunhofer de http www quadriga channel model de A Fraunhofer Heinrich Hertz Institute QuaDRiGa v1 2 3 307 Contributors Editor Fraunhofer Heinrich Hertz Institute Wireless Communication and Networks Einsteinufer 37 10587 Berlin Germany Contributing Authors Stephan Jaeckel Leszek Raschkowski Kai Borner and Lars Thiele Fraunhofer Heinrich Hertz Institute Frank Burkhardt and Ernst Eberlein Fraunhofer Institute for Integrated Circuits TIS Grants and Funding This work was supported by e the European Space Agency ESA in the Advanced Research in Telecommunications Systems ARTES programme under contract AO 1 5985 09 08 NL LvH Acronym MIMOSA EHB 13 http telecom esa int telecom www object index cfm fobjectid 31061 e the German Federal Ministry of Economics and Technology BMWi in the national collaborative project IntelliSpektrum under contract 01ME11024 http www intellispektrum de Acknowledgements The authors thank G Sommerkorn C Schneider M Kaeske Ilmenau University of Technology IUT Ilmenau Germany and V Jungnickel Heinrich Hertz Institute HHI Berlin Germany for the
71. R in rad The dimensions correspond to the MT the path number and the sub path number random_pol Random phasors for the WINNER polarization coupling method The dimensions correspond to polarization matrix index 1 3 2 4 the subpath number and the MT subpath_coupling A random index list for the mutual coupling of subpaths at the Tx and Rx The dimensions correspond to the subpath index 1 20 the angle AoD AoA EoD EoA the path number and the MT Methods h_cb channel_builder h_parset Description Creates a new channel_builder object Input h_parset A parameter_set object Output h_cb A channel_builder object h_channel get_channels Description Generates the channel coefficients This is the main function of the channel_builder Output h_channel An array of channel objects Copyright Fraunhofer Heinrich Hertz Institute 42 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE h_channel channel_builder get_los_channels h_parset Description Generates channel coefficients for the LOS path only This function generates static coefficients for the LOS path only This includes the following properties antenna patterns orientation of the Rx if provided polarization rotation for the LOS path plane wave approximation of the phase path loss shadow fading No further features of QuaDRiGa are used
72. SA Satellite to Mobile Parameters for Urban Propagation Elevation range from 16 to 25 Parameters were extracted from terrestrial measurement using a high attitude platform MIMOSA_25 35_LOS MIMOSA_25 35_NLOS MIMOSA Satellite to Mobile Parameters for Urban Propagation Elevation range from 25 to 35 Parameters were extracted from terrestrial measurement using a high attitude platform MIMOSA_35 45_LOS MIMOSA_35 45_NLOS MIMOSA Satellite to Mobile Parameters for Urban Propagation Elevation range from 35 to 45 Parameters were extracted from terrestrial measurement using a high attitude platform 2 4 1 Description of the Parameter Table The QuaDRiGa channel model is a generic model That means that it uses the same method for generating channel coefficients in different environments E g the principal approach is exactly the same in a cellular network and in a satellite network The only difference is the parametrization for both cases Each envi ronment is described by 55 individual parameters These parameters are stored in configuration files that can be found in the subfolder named config in the main channel model folder The parameters and values can be describes as follows Copyright Fraunhofer Heinrich Hertz Institute 48 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE e No Clusters This value describes the number of clusters Each cluster is
73. Unified linear arrays composed of 8 omni antennas vertical polarization with 10 cm element distance Input array_type One of the above array types element The element numbers for which this functions is applied If no element number is given the function creates a new array and delete the old elements in the array phi_3dB The 3dB beam width in azimuth direction used only for custom array type theta_3dB The 3dB beam width in elevation direction used only for custom array type rear_gain The isotropic gain linear scale at the back of the antenna used only for custom array type Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 21 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE h_array mse mse_pat array import_pattern fVi fHi correct_phase accuracy max_num_elements azimuth_grid elevation_grid verbose Description This function converts any antenna field pattern into a QuaDRiGa antenna array object The conversion needs at most 4 elements for each input field pattern This value is doubled if the inputs are complex valued Input fVi correct _phase accuracy max_num_ elements azimuth_grid elevation_grid verbose The field pattern s for the vertical polarization given in spherical coordinates The first dimension corresponds to the elevation angle ranging from 90 to 90 degrees The second dimension is for the az
74. ack par h_parset A matrix of parameter_set objects Rows correspond to the scenarios columns correspond to the transmitters See Section 2 2 5 h channel h_parset h_cb get_channels sampling rate check_parfiles Description Calculate the channel coefficients This is the most simple way to create the channel coefficients This function executes all steps that are needed to generate the channel coefficients Hence it is not necessary to use use the parameter_set or channel_builder objects Input sampling rate channel update rate in s This parameter is only used if a speed profile is provided in the track objects Default value 0 01 10 ms check_parfiles Enables 1 default or disables 0 the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves some execution time Output h_channel A vector channel objects See Section 2 2 7 h_parset A matrix of parameter_set objects Rows correspond to the scenarios columns correspond to the transmitters See Section 2 2 5 h_cb A vector of channel_builder objects See Section 2 2 6 Copyright Fraunhofer Heinrich Hertz Institute 36 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE map posx posy power_map scenario usage res xs ys xe ye tx_power
75. ad Tis Delay of the cluster at snapshot position s PL Path Loss linear scale PLjapj 10 logyg PL is for logarithmic scale SD Sample Density in units of samples per half wave length SF Shadow Fading linear scale SFj p 10 logy9 SF is for logarithmic scale 0 Elevation angle 0 for arrival EoA 6 for departure EoD v0 Polarization rotation angle used for the coupling matrix M Per Cluster Shadow Fading ap bE Filter coefficients c Speed of Light C L K Correction function for the initial angles C K Correction function for the initial path delays CAoA Cluster wise azimuth spread of arrival d Distance also used a path length dy Decorrelation distance dr Total length of the wave travel direction vector r dLos Distance between the receiver position and the scatterers for the LOS cluster dpx Distance in m between two adjacent pixels of the autocorrelation map B fe Carrier Frequency fs Sampling Rate in units of samples per meter fr Sampling Rate in units of samples per second Irt Complex amplitude of a tap between Tx antenna t and Rx antenna r hr tn Channel coefficient in frequency domain K Ricean K factor linear scale Kjqpj is for logarithmic scale k Filter coefficient index L Number of clusters or paths Copyright Fraunhofer Heinrich Hertz Institute 6 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 List of Acronyms l Cluster or path index l 1 L m Subpath index m
76. agot ea ee eG 60 10 Illustration of the map based generation of the DS aoaaa aa 61 1l Cpt mnei Cala ES ee enea et n eee a a k a de d e a h ee es 65 12 Illustration of the calculation of the drifting angles o oo aoa 66 13 Illustration of the angles and vectors used for the computation of the geometric LOS polarization 68 14 Illustration of the SF drifting along a terminal track aoaaa 72 15 Top Illustration of the overlapping area used for calculating the transitions between segments step G Bottom Illustration of the interpolation to to obtain variable MT speeds step H 73 16 Distribution of the users in the scenario se sop osam oam e boed ee ee aa a e 75 17 Comparison of input values and simulation results oaoa ee 76 18 Scenario setup for the comparison of simulated and measured data o oo oa a 79 19 SO PDP at the simulated track 2 0 sr cenne ek OS Oa ea ee ee ae a ah 79 20 Results for the measurement based simulation tutorial 2 204 81 21 Receiver track for the satellite channel tutorial 0 0 00 02 0000 5 83 22 Antenna patterns for the satellite channel tutorial 1 0 a a ee 84 23 Results for the satellite channel tutorial 2 0 0 0 02 a 89 24 Scenario setup for the drifting phases tutorial 2 2 0 0 02 eee eee 90 25 Cluster delays vs Rx position drifting phases tutorial 000 91 26 Drifting phases and Tx power vs Rx position drifting ph
77. andom satellite position Astra 2 seen from Berlin 4 The distance only needs to be big enough to ensure insignificant changes 4 in the reception angle on the ground sat_el 28 4 4 Elevation angle sat_az 161 6 Azimuth angle South 180 degree rx_latitude 51 4 Latitude of the Ra 4 Approzimate the satelite distance for GEO orbit dist_x 35786 rx_latitude 90 6384 h km dist_y 1 rx_latitude 90 6384 km sat_dist sqrt dist_x 2 dist_y 2 hk km sat_dist sat_dist 1e3 m Transform angles to Cartesian coordinates sat_x sat_dist cosd sat_el cosd sat_az 90 sat_y sat_dist cosd sat_el sind sat_az 90 sat_z sat_dist sind sat_el x We also turn the antenna of the satellite so it points to the receiver a rotate_pattern sat_el y a rotate_pattern 270 sat_az z 4 Set the satellite position in the layout 1 tx_position sat_x sat_y sat_z l track t 4 Set the track for the receiver 1l tx_array a 4 Set the t array 1 rx_array i ion 4 Set the rav_array Setting up scenario parameters Next the large scale parameters are set The first line calls create_parameter_sets a built in function that processes the data in the layout and returns a new pa rameter_set object p p is an array with two elements One of them contains all the parameters for the good state LOS and one for the bad state NLOS
78. anges are calculated deterministically based on the arrival angles This results in a realistic Doppler spectrum The temporal evolution of the channel is modeled by two effects e drifting and e birth and death of clusters Drifting see Section 3 2 3 occurs within a small area about 20 30 m diameter in which a specific cluster can be seen from the MT Within this area the cluster position is fixed Due to the mobility of the terminal the path length resulting in a path delay and arrival angels change slowly Longer time evolving channel sequences need to consider the birth and death of scattering clusters as well as transitions between different propagation environments We address this by splitting the MT trajectory into segments A segment can be seen as an interval in which the LSPs e g the delay and angular spread do not change considerably and where the channel keeps its wide sense stationary WSS properties Thus the length of a segment depends on the decorrelation distances of the LSPs We propose to limit the segment length to the average decorrelation distance Typical values are around 20 m for LOS and 45 m for NLOS propagation In the case where a state does not change over a long time adjacent segment must have the same state For example a 200 m NLOS segment should be split into at least 4 NLOS sub segments A set of clusters is generated independently for each segment However since the propagation channel does not chang
79. antenna element separately An illustration of the angles and their relations is given in Fig 12 top jth The first delay is always zero due to 32 Hence we calculate the total length of the path as di T c ro 46 where ro is the distance between the Tx and the initial Rx location and c is the speed of light We assume that all sub paths have the same delay and thus the same path length However each sub path has different arrival angles 7 Lm L m We transform those angles a vector 4 9 in Cartesian coordinates and obtain cos Of m COs OP a al m 0 im l m0 a i sin Fn cos 6 47 ae sin 0 Copyright Fraunhofer Heinrich Hertz Institute 66 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION We approximate the drifting at the Rx by assuming only a single reflection Hence Tx Rx and LBS form a triangle Since we know d ro and Aj m 9 we can apply the cosine theorem to calculate the distance aj m 0 between the Rx and LBS bi m 0 ro Eg lazm ol 2 ro az m o cos Bi m 0 2 2 2 Ta di a m o rol arm o 2 aim ol ro Aim o d ro 2 O Eae 48 2 di rf imo Now we can calculate the vector a jm s for the Rx antenna element r at snapshot s The element position includes the orientation of the antenna array with respect to the moving direction of the Rx Hence the vector qr s points from the initial Rx location to the rt anten
80. arameters The channel model operates on a position based sample grid That means that the channel_builder generates one CIR for each position on the track In practise however a time continuous evolution of the CIR is often needed This can be interpolated from the position based grid provided that the spatial sample theorem is not violated i e the channel needs to be sampled at least twice per half wave length In order to do that enough sample points are needed along the track INTERPOLATE_POSITIONS calculates the missing sample points and places them equally spaced along the track This corresponds to a constant speed when evaluating the output CIRs The required value for samples_per_meter can be obtained from the simulation_parameters object Input samples_per_meter the samples per meter e g from simulation_parameters samples_per_meter Copyright Fraunhofer Heinrich Hertz Institute 33 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE set_scenario scenario probability seg_length_min seg_length_mu seg_length_std Description Assigns random scenarios and creates segments This function can be used to create segments along the trajectory and assign scenarios to the segments If there are less than 3 input arguments i e only scenario and or probability is given then no segments will be created To create segments wit
81. ario Scenarios for each segment along the track This variable contains the scenario names for each segment as a cell array of strings A list of supported scenarios can be obtained by calling parameter_set supported_scenarios If there is only one transmitter i e one base station the cell array has the dimension 1 x no_segments For multiple transmitters the rows of the array may contain different scenarios for each transmitter For example in a multicell setup with three terrestrial base stations the propagation conditions may be different to all BSs The cell arrays than has the dimension 3 x no_segments par Manual parameter settings This variable contains a structure with LSPs This can be used for assigning LSPs directly to the channel builder e g when they are obtained from measurements The structure contains the following fields ds The delay spread in s per segment kf The Ricean K Factor in dB per snapshot pg The effective path gain in dB excluding antenna gains per snapshot asD The azimuth angle spread in deg per segment at the transmitter asA The azimuth angle spread in deg per segment at the receiver esD The elevation angle spread in deg per segment at the transmitter esA The elevation angle spread in deg per segment at the receiver xpr The NLOS cross polarization in dB per segment If there is only a subset of variables e g the angle spreads are missing then the correspo
82. asA_ds 0 6100 asA_sf 0 5600 asD_sf 0 ds_sf 0 4300 asD_asA 0 asD_kf 0 asA_kf 0 4400 ds_kf 0 4600 sf_k 0 3000 esD_ds 0 esA_ds 0 0500 esA_sf 0 1800 esD_sf 0 esD_esA 0 esD_asD 0 esD_asA 0 esA_asD 0 esA_asA 0 1500 esD_kf 0 esA_kf 0 0300 Note that the values are given for a log normal distribution Thus the RMSDS in nanoseconds follows from 10 p S1 scenpar DS_mu 1e9 ans 31 6228 Each parameter on that list can be changed by just assigning it a new value Here we set the number of clusters for the LOS scenario to 7 Note that the default settings are stored in files in the sub folder config of the channel model folder Here the default settings can be permanently set After a change the parameters of the segments need to be updated This is done by calling the update_parameters method p Sl scenpar NumClusters 7 p update_parameters Parameters 00000000000000000000000000000000000000000000000000 24 seconds When update_parameter is called the specific parameters for each segment are generated E g each segment gets assigned a RMS Delay Spread and other values which are drawn from the statistics defined in scenpar For the LOS segments the individual RMSDS values for each segment are rmsds p S1 ds 1e9 average mean p S1l ds 1e9 Copyright Fraunhofer Heinrich Hertz Institute 86 eMail quadriga hhi fraunhofer de
83. ases tutorial 92 27 Phases and Tx power vs Rx position without drifting drifting phases tutorial 93 28 Scenario setup for the time evolution tutorial 1 ee 95 29 Received power on the circular track time evolution tutorial 0 96 30 Received power on the linear track time evolution tutorial 0 0 98 31 Scenario setup for the speed profile tutorial 2 ee 100 32 Received power and 2D PDP for the speed profile tutorial 2 100 33 Movement profile left and interpolated PDP right 102 34 Polarimetric dipole antenna patterns for different orientations 004 103 Go scene la ao eh a eR eee ee OE Gy BR ek Oe ee eee ee he 104 36 Results from the geometric polarization tutorial 2 2 0 0 00 eee ee 105 37 RHCP 7 LHCP antenna patterns cs bce hee ke Se SG He eae a EASE Ee EOS 108 38 Scenario overview manual parameter selection 2 000 eeee 110 39 Power along the track manual parameter selection 0 00 eee eens 112 40 DS along the track manual parameter selection 2 00 eee 112 Copyright Fraunhofer Heinrich Hertz Institute 4 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 List of Acronyms List of Tables 1 11 12 13 QuaDRiGa System Requirements a sooo a a 10 Parameter sets provided together with the standard software ooo a a 48 Offset Angle of the m Sub
84. ate_pattern 90 x 2 Set the coupling between the elements The Tx signal for the first element is shifted by 90 degree out of phase and put on the second element The signal for the second element is shifted by 90 degree and copied to the first element Both antennas thus radiate a RHCP and a LHCP wave coupling 1 sqrt 2 1 1 1j 1j SN NT w Create a copy of the array for the receiver a copy_objects coupling 1 sqrt 2 1 1 1j 1j oox x Rotate the receive antenna array to face sky wards b rotate_pattern 90 y b visualize 4 Plot the pattern of the Rax Antenna Element 1 Element 2 Vertical Vertical Horizontal Horizontal 12 9 6 3 0 3 6 12 9 6 3 0 3 6 Attenuation dB Attenuation dB Figure 22 Antenna patterns for the satellite channel tutorial Copyright Fraunhofer Heinrich Hertz Institute 84 eMail quadriga hhi fraunhofer de 16 NH H U Ne Oo A oO MND wwn nnn nnn bd rar QuaDRiGa v1 2 3 307 A TUTORIALS Setting up the Layout In this step we combine the track the antennas and the position of the satellite into a simulation layout A layout object contains all the geometric information that are necessary to run the simulation First we define the position of the satellite Since the model uses Cartesian coordinates we have to transform the position of the satellite first 1 layout s 4 Create a new layout Z Choose a r
85. aunhofer Heinrich Hertz Institute 80 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS The final plot Fig 20 bottom right shows the distribution PDF of the RMS delay spread for both the LOS and NLOS segments bins 0 0 03 3 ds_los hist ds los 1e6 bins cn no_snap 100 ds_nlos hist ds setdiff 1 cn no_snap los 1e6 bins cn no_snap 100 figure bar bins ds_los ds_nlos axis 0 1 5 0 ceil max ds_los ds_nlos grid on colormap Cool title Empirical PDF of the LOS and NLOS RMSDS xlabel sigma_ tau mus ylabel Probability legend LOS NLOS 1 close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 Position dependent power Empirical PDF of the LOS and NLOS power r r T i 10 T T T T T T r r 85 al Los oF HES NLOS 90 8p J 95 74 4 100 amp 5 D z 105 z 5h J 110 4l J 115 3H 4 120 2f 125 ai i i B E f oH i j i 0 100 200 300 400 500 120 115 110 105 100 95 90 85 Track m P otal dB Position dependant delay spread Empirical PDF of the LOS and NLOS RMSDS 2 5 12L Los Ml NLOS 2 3 3 8 1 5 2 amp E z 3 o l a A 0 5 200 300 400 500 0 0 5 Track m 0 100 o ps Figure 20 Results for the measurement based simulation tutoria
86. ccess these files A 1 Network Setup and Parameter Generation The channel model class parameter_set generates correlated values for the LSPs The channel builder then uses those values to create coefficients that have the specific properties defined in parameter_set One important question is therefore Can the same properties which are defined in parameter_set also be found in the generated coefficients This is an important test to verify if all components of the channel builder work correctly Channel model setup and coefficient generation We first set up the basic parameters We do not need drifting here since no time varying channels are generated close all clear all set 0 defaultTextFontSize 14 set 0 defaultAxesFontSize 14 s simulation_parameters s center_frequency 2 53e9 s sample_density 2 s use_absolute_delays 1 s drifting_precision 0 We have one transmitter and 250 receiver positions Each receiver gets a specific channel However the receivers LSPs will be correlated We use omni directional antennas at all terminals Copyright Fraunhofer Heinrich Hertz Institute 74 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS layout s no_rx 250 randomize_rx_positions 200 1 5 1 5 1 200 m radius 1 5 m Re height track set_scenario BERLIN_UMa_NLOS 1 tx_position 3 25 4 25 m tx height 1 tx_array generat
87. cients PL_model winner_los PL_A1 26 PL_B1 25 PL C1 20 PL_D1 0 PL_E1 0 PL_sigi 4 PL_A2 40 PL_B2 9 27 PL C2 6 PL_D2 14 PL_E2 14 PL_sig2 6 You can create you own scenario by editing this file and saving it under a new filename in the config Folder The file ending must be conf The filename then is also the scenario name and the settings can be accessed from inside MATLAB as described above Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 53 QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 Technical Documentation An overview of the modeling steps is given in Fig 5 The user provides the network layout i e the positions of the BSs antenna configurations downtilts the positions and trajectories of the MTs and the propagation scenarios The channel coefficients are then calculated in seven steps which are described in sections 2 4 and 3 2 Much of the modeling approach is inspired by the WINNER model KMH 07 Major extensions concerning the time evolution have been made in steps D and G In order to make those extension work properly changes were also required in the other parts For example time evolution requires a more detailed description of the mobility of the terminals which is solved by assigning tracks i e ordered lists of positions to the MTs Updates of the channel coefficients are then triggered at fixed snapshot pos
88. ck_length or direction is not specified then the default track is 1 m long and has a random direction Input track_length The track length in m Default length is 1 m direction specifies the driving direction in rad The default is random generate circular track_length direction Description Creates a circular track with given length and starting direction Input track_length The circumference of the circular track in m Default is 62 8 m direction The starting point on the circle in rad Positive values define the travel direction as counter clock wise and negative values as clock wise E g 0 sets the start point in the east of the circle traveling north 27 sets it in the east traveling south The default is random Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 31 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE generate street track_length direction street_length_min street_length_mu street_length_std curve_radius turn_probability Description Emulates a drive route through a city grid The mobile terminal starts at point 0 going into a specified direction The trajectory grid is build from street segments The length of each street is specified by the parameters street_length_min street_length_mu and street_length_sigma At the end of a street i e at a crossing the terminal
89. coefficients of adjacent segments are combined merged This includes the birth death process of clusters Additionally different speeds of the terminal can be emulated by interpolation of the channel coefficients The channel coefficients together with the path delays are formatted and returned to the user for further analysis 1 6 Description of modeling of different reception conditions by means of a typical drive course This section describes some of the Key features of the model using a real world example A detailed introduction with a variety of tutorials test cases and interface descriptions then follows in section A The later part of the document then focusses on the mathematical models behind the software and the assumptions made a atellit RHCP signal Figure 2 Typical driving course From home to woodland parking site on the village outskirts The different effects along the track can be summarized as follows Ole Ne Start Environment Urban LOS reception of satellite signal LOS NLOS Change NLOS LOS Change Turning off without change in reception condition LOS Stopping at traffic light LOS Turning off with change of reception condition LOS gt NLOS Crossing side Street NLOS short LOS NLOS Copyright Fraunhofer Heinrich Hertz Institute 15 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW 8 Structural change in the environmen
90. corner xe x coordinate in m of the bottom right corner ye y coordinate in m of the bottom right corner tx_power A vector of tx powers in dBm for each transmitter in the layout This power is applied to each transmit antenna in the tx antenna array By default if tx_power is not given 0 dBm are assumed Output map A cell array containing the power map for each transmitter in the layout The maps have the dimensions y coordinate x coordinate Rx antenna Tx antenna posx Vector with the x coordinates of the map posy Vector with the y coordinates of the map randomize_rx_positions max_dist min_height max_height track_length Description Generates random Rx positions and tracks Places the users in the layout at random positions Each user will be assigned a linear track with random direction The random height of the user terminal will be in between min_height and max_height Input max_dist the maximum distance from the layout center in m Default is 50 m min height the minimum user height in m Default is 1 5 m max height the maximum user height in m Default is 1 5 m track_length the length of the linear track in m Default is 1 m Copyright Fraunhofer Heinrich Hertz Institute 37 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE pairs power set_pairing method threshold tx_power check_parfiles
91. d e g to assemble a 45 crosspolarized array out of single dipoles This functionality is provided here Note Calling rotate_pattern will always remove the common phase from the field patterns Call estimate_common_phase before calling rotate_pattern to extract the common phase information Input deg The rotation angle in degrees ranging from 180 to 180 rotaxis The rotation axis specified by the character x y or z element The element numbers for which this interpolation is done is applied If no element number is given the interpolation is done for all elements in the array usage The optional parameter usage can limit the rotation procedure either to the pattern or polarization Possible values are e 0 Rotate both pattern polarization default e 1 Rotate only pattern e 2 Rotate only polarization Output cp The common phase of the field pattern set_grid azimuth_grid elevation_grid Description Sets a new grid for azimuth and elevation and interpolates the pattern This function replaces the properties azimuth_grid and elevation_grid of the antenna object with the given values and interpolates the antenna patterns to the new grid Input azimuth_grid Azimuth angles in rad were samples of the field patterns are provided The field patterns are given in spherical coordinates This variable provides the azimuth sampling angles in radians ranging from
92. d this involves an interpolation step As a standard computationally inexpensive procedure we use linear interpolation Alternatively more advanced techniques based on the effective aperture distribution function EADF can be used NKS 07 The second step of the transformation takes the polarization into account We first take the antenna orientation vector o and apply the rotation matrix Q to obtain 0 0 Q o 14 This vector 6 is the new orientation vector of the transformed pattern Next we calculate a rotation matrix that accounts for the changed polarization characteristics of the rotated antenna pattern In principal the following procedure emulates an antenna measurement like in an anechoic chamber The virtual transmitter is placed south of our test antenna The wave travel direction r thus lies in y direction We thus rotate the orientation vector of our antenna so that it matches the wave travel direction The polarization vector pr results from a projection of the orientation vector o on the plane perpendicular to the travel direction see Fig 13 for details 1 We rotate the orientation vector 6 by p 7 2 in azimuth direction and q in elevation direction to match the orientation of the transmitter cos p sinp 0 ot cosq sinp cosq cosp sing 6 15 sing sinp sing cosp cosq 2 We calculate the projection of the orientation vector on the projection plane Since the projection plane lies in the x z plan
93. d coefficients and compare them with the initial ones which were generated by the parameter set object P The values in P can be seen as a request to the channel builder and the values in the generated coefficients C as a delivery We first calculate the SF from the channel data by summing up the power over all 20 taps We see that the values are almost identical sf sum mean abs coeff 2 3 2 figure plot 35 35 35 35 k hold on plot 35 35 3 35 35 k plot 35 35 3 35 35 k plot 10 logi0 p sf 10 logi0 sf hold off axis 25 25 25 25 J legend Equal 3dB 4 xlabel SF_P dB ylabel SF_C dB title Shadow Fading Requested vs generated value Shadow Fading Requested vs generated value K Factor Requested vs generated value 25 T T T TF 30 T T T T T P 20 A Lee 15 SA aof ue 10 hae E gig a ar a 10 he 5 EEZ 7 4 P Pd m Fid m Pag ge of ae Be cal ose A gt 2 RA 5 ae ee z 7 Zz 10 7 7 4 10 wo fee 1 ge 15 e ee ae ae 2f 2 20tl Equal yh ge Equal 3dB Oe 4 3dB 25 n fi fi L T 30 1 1 1 T 20 1 0 10 20 30 20 0 20 30 SF dB KF dB P P Delay Spread Requested vs generated value 6 Delay Spread difference vs K factor 1 5 l T i 7 r 7
94. e omni l rx_array 1 tx_array 1l visualize 0 view 33 60 Tx Position A Tx Antenna O Rx Position V_ Rx Antenna Rx Track Y Position 200 200 X Position Figure 16 Distribution of the users in the scenario We set up the scenario such that there is no XPR I e all vertical polarized paths will remain vertical after a reflection The same result would be achieved with a perfectly X polarized array at the receiver and summing up the power over all elements We further increase the KF to have a wider spread This allows us to study the parameters at a wider range when evaluating the results p l create_parameter_sets 0 p plpar p scenpar xpr_mu 100 h Disable XPR p scenpar xpr_sigma 0 p scenpar KF_mu 5 4 Increase KF Range p scenpar KF_sigma 15 p scenpar DS_mu 1log10 0 6e 6 4 Median DS 600 ns p scenpar DS_sigma 0 3 4 300 1200 ns range p update_parameters c p get_channels coeff squeeze cat 1 c coeff delay permute cat 3 c delay 3 1 2 Parameters 00000000000000000000000000000000000000000000000000 J 5 seconds Channels 00000000000000000000000000000000000000000000000000 J 8 seconds Copyright Fraunhofer Heinrich Hertz Institute 75 eMail quadriga hhi fraunhofer de 10 QuaDRiGa v1 2 3 307 A TUTORIALS Results and discussion In the following four plots we extract parameters from the generate
95. e Name of the track initial position Position offset will be added to positions This position is given in global cartesian coordinates x y and z component in units of m The initial position normally refers to the starting point of the track If the track has only one segment it is also the position for which the LSPs are calculated The initial position is added to the values in the positions variable no snapshots Number of positions on the track positions Ordered list of position relative to the initial position QuaDRiGa calculates an instantaneous channel impulse response also called snapshot for each po sition on the track movement_profile Time in sec vs distance in m for speed profile QuaDRiGa supports variable terminal speeds This is realized by interpolating the channel coefficients at the output of the model The variable track movement_profile describes the movement along the track by associating a time point with a distance point on the track An example is t movement_profile 0 7 5 0 6 0 20 20 dist t interpolate_movement 1le 3 ci cn interpolate dist t get_length See also the tutorial Applying Varying Speeds Channel Interpolation in Section A 6 for more details no_segments Number of segments or states along the track segment_index Starting point of each segment given as index in the positions vector scen
96. e EERE es 39 226 Class chamel builder o 2 6 24 5 eee gene Pee ew ee eS aE Sa ee eo 42 22 4 Class Channel 46 cee ee eee Se Rm wR eR Gwe eS ee ae eS 44 Mo Data PIO e eaoaai tis we Go ee Gee Re a ate ange ae ae Ek a DAN a k A 47 24 Scenario Speciic Parameters s la e sos so c anam r c gea e aa a a a a a eG 48 24 1 Description of the Parameter Table es ca ac a saspe aletan Eaa eee ees 48 24 2 Acting New Scenarios ne oma or OR a ee a we ee a eee a 52 3 Technical Documentation 54 3 1 Tracks Scenarios Antennas and Network Layout aooo o e e a ee 54 31 1 Drops and Segmente s gt oa ce doa Sk RG a Re ae eR we ee 54 3 12 Sample Density vs Sample Rate ooo se sarea digua wda t d aaa a eS 55 343 The Antenne Model o sa saos a a 665 a a gy a eee Ea eee A 56 3 1 4 Generation of Correlated Large Scale Parameters aoao a 59 3 2 Calculation of Channel Coefficients aooo aa a 2 ee 62 3 2 1 Initial Delays and Cluster Powers aooaa a a 62 3 2 2 Departure and Arrival Angles o s sc ccocoa occ ke tad rag ee 63 3 2 3 Drifting of Angles Delays and Phases oaoa aa e 66 3 2 4 Geometric Polarization s lt sacara reat Kdo osama Kele iata etai 68 3 2 5 Calculation of the Channel Coefficients ooo a 71 32 6 Path Gain Shadow Fading and K Factor 2 446620 54 25 04 a ea ees 71 3 2 7 Transitions between Sememi o so 6 ee hoe Eo a eee ae ee ee ee a 72 3 2 8 Postprocessing Variable Speeds ooa a a eee ew eee ee ee es 73 A Tut
97. e due to the placement of the transmitter we simply omit the y component of o switch Copyright Fraunhofer Heinrich Hertz Institute 57 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION the x and z component and normalize the resulting vector to unit length The switching is done to obtain the same orientation as in 7 Oz Oy 1 Gales val 3 We define the transmitter to be either perfectly vertical p L 0 or perfectly horizontal p 0 1 As a consequence the product p p selects either the vertical or horizontal component of p and a can be calculated to a arctang Pry Pre arctang of of 17 4 a is the angle between the projection of the orientation vector and the E component of the transmit polarization Since the transmitter is vertically polarized 6 is 0 The angle 3 comes from the rotated field pattern response 13 Br arctan u 6 0 Fv 6 18 5 The difference between a and 6 is the rotation angle J which is used to calculate the polarization effects on the pattern The rotated pattern then notes 9 p a a e a 19 sin cosv Modeling circularly polarized antennas In many applications such as satellite communications circular polarization is needed A straight forward extension would use complex coefficients in the field patterns nali neal a are the Jones vectors for the RHCP and LHCP signal respectively However an es
98. e need the parameters u o and A to define the distributions These spreads are translated into specific angles for each multipath cluster Additionally we assume that clusters are the source of several multipath components that are not resolvable in the delay domain Thus these sub paths do not have specific delays but they have different departure and arrival angles Thus we need an additional parameter cg for each of the four angles that scales the dimensions of the clusters in 3D space See Sec 3 2 2 for details Cross polarization Ratio XPR The XPR defines how the polarization changes for a multipath component I e the initial polarization of a path is defined by the transmit antenna However for the NLOS components the transmitted signal undergoes some diffraction reflection or scattering before reaching the receiver The XPR in dB is assumed to be normal distributed where u and o define the distribution We translate the XPR in a polarization rotation angle which turns the polarization direction A XPR value where a value of Inf means that the axis remains the same for all NLOS paths I e vertically polarized waves remain vertically polarized after scattering On the other hand a value of Inf dB means that the polarization is turned by 90 In case of 0 dB the axis is turned by 45 i e the power of a vertically polarized wave is split equally into a H and V component The following table gives an overview of the parameters
99. e same orientation as in 7 EES o 3 We get the transmit polarization vector p by normalizing the field pattern vector F 7 to unit length F is already in the same plane as p due to the coordinate rotation P F F 64 4 We calculate the angle a between the two vectors a arccos pi pe 65 5 We obtain the angles 6 and 8 from the field patterns of the transmitter and receiver respectively 8 is the angle between the z axis of the polarization plane and the polarization vector Note that Ez in Fig 13 is the vertical and Ey is the horizontal polarized component Bt arctan Fin 2 6 Fiv 07 07 66 br arctang Fy 4 6 Frv 0 0 67 6 The difference between the angles 6 and 6 is the polarization mismatch 8 between the receiver and the transmitter if both antenna elements were aligned on the same optical axis The angle a however takes the different orientation of the receive antenna into account We thus need to rotate the polarization of the receiver by an angle of Ort Br a 68 7 The channel coefficients are now calculated according to 6 where the polarization coupling matrix M notes 69 M v 1 cosU sin d s sinV COS r Model for the NLOS components For the NLOS components the transmitted signal undergoes some diffraction reflection or scattering before reaching the receiver Following the common Fresnel formula in electrodynamics the po
100. e scenario names Those can be used in track scenario file names The names of the configuration files for each scenario file_dir The directory where each file was found You can place configuration file also in you current working directory update_parameters force Description Generates the LSP maps and updates the parameters for all terminals This function calculates correlated large scale parameters for each user position Those parameters are needed by the channel builder class to calculate initial parameters for each track or segment which are then evolved into time varying channels By default update_parameters reads the values given in the track objects of the layout If there are no values given or if parts of the values are missing the correlation maps are generated to extract the missing parameters Input force Changes the behavior of the function force 0 default Tries to read the parameters from layout track par If they are not provided or it they are incomplete they are completed with values from the LSP maps If the maps are invalid e g because they have not been generated yet new maps are created force 1 Creates new maps and reads the LSPs from those maps Values from layout track par are ignored Note that the parameters pg and kf will still be taken from layout track par when generating channel coefficients force 2 Creates dumm
101. e significantly from segment to segment we need to include correlation This is done by so called parameter maps see Section 3 1 4 The maps ensure that neighboring segments do not have significantly different propagation characteristics For example measurements show that the shadow fading the average signal attenuation due to building trees etc is correlated over up to 100 m Hence we call all channel characteristics showing similarly slow changes LSPs With a segment length of 20 m two neighboring segments of the same state will have similar receive power To get the correct correlation QuaDRiGa calculates a map for the average received power for a large area The received power for two adjacent segments is then obtained by reading the values of the map This map based approach also contains cross correlations to other LSPs such as the delay spread For example a shorter delay spread might result in a higher received power Hence there is a positive correlation between power and delays spread which is also reflected in the maps To get a continuous time series of channel coefficients requires that the paths from different segments are combined at the output of the model In between two segments clusters from the old segment disappear Copyright Fraunhofer Heinrich Hertz Institute 13 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW and new cluster appear This is modeled by merging the chan
102. e6 xlabel Delay mus set gca YTick 1 cn 2 no_snap 8 cn 2 no_snap set gca YTickLabel 0 cn 2 no_snap 8 cn 2 no_snap cn 2 no_snap 20 ylabel Distance from start point m title PDP for the linear track with drifting Last we plot the same results for the linear track without drifting Fig 30 right Note that the LOS delay is not smooth during segment change There are two jumps at 6 m and again at 13 5 m h dn 2 fr 100e6 512 h squeeze h pdp 10 logi0 abs ifft h 1 72 figure imagesc pdp 1 256 caxis max max pdp 50 max max pdp 5 J colorbar cm colormap hot colormap cm end 1 1 set gca XTick 1 32 255 set gca XTickLabel 0 32 256 100e6 1e6 xlabel Delay mus set gca YTick 1 cn 2 no_snap 8 cn 2 no_snap set gca YTickLabel 0 cn 2 no_snap 8 cn 2 no_snap cn 2 no_snap 20 ylabel Distance from start point m title PDP for the linear track without drifting close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 Copyright Fraunhofer Heinrich Hertz Institute 97 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS PDP for the linear track with drifting ste al aed Distance from start point m 0 0 32 0 64 0 96 1 28 1 6 1 92 2 24
103. ect However this requires that the orientation vectors are set correctly i e you need to call estimate_pol_vector first when importing measured patterns Input element The element numbers for which this functions is applied If no element number is given the common phase is estimated for all elements in the array pol_vector err estimate_pol_vector element verbose Description Estimates the orientation vector from the patterns This function estimates the orientation vector from the field patterns This allows the use of mea sured patterns in QuaDRiGa where the orientation vector is unknown Results are also stored in the pol_vector property of the array object Input element The element numbers for which this functions is applied If no element number is given the common phase is estimated for all elements in the array verbose Enables 1 default or disables 0 the progress bar Output pol_vector The orientation of the electric field in 3D local cartesian coordinates err provides information on how well the pol vector matches the pattern It scales from 0 to 1 where 0 is a perfect match Copyright Fraunhofer Heinrich Hertz Institute 26 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE generate array_type element phi_3dB theta_3dB rear_gain Description Generates predefined arrays Array Types omni dipole half wave dipole patch custom xpol rhcp di
104. ed Scenario This script recreates a measured drive test from the Park Inn Hotel at Berlin Alexanderplatz The transmit ter was at the rooftop of the hotel while the mobile receiver was moving south on Grunerstra e A simplified version of the scenario is recreated in the simulation where the scenarios along the track were classified by hand Channel model setup and coefficient generation First we set up the channel model set 0 defaultTextFontSize 14 set 0 defaultAxesFontSize 14 RandStream setGlobalStream RandStream mti9937ar seed 1 close all clear all s simulation_parameters Basic simulation parameters s center_frequency 2 185e9 s sample_density 2 s use_absolute_delays 1 t track linear 500 135 pi 180 4 Track with 500m length direction SE t initial_position 120 120 0 4 Start position t interpolate_positions 1 hk Interpolate to 1 sample per meter t segment_index 1 45 97 108 110 160 190 215 235 245 280 295 304 330 400 430 l 4 Set segments states S1 MIMOSA_10 45_LOS Sn MIMOSA_10 45_NLOS t scenario Sn S1 S5n S1 S5n Sn Sn S1 S5n S1 5n 81 Sn Sn Sn Sn t interpolate_positions 3 1 layout s 1 tx_position 0 0 125 l track t Set the position of the Te Set the ra track ax Generate Tx antenna 30 deg Tilt point southwards 1l tx_array array rhcp lhcp dipole tx_array rotate_pattern 30 y
105. el Models Stephan Jaeckel Fraunhofer Heinrich Hertz Institute Wireless Communication and Networks Einsteinufer 37 10587 Berlin Germany e mail stephan jaeckel hhi fraunhofer de DE HC HE HE HE FE JE AE JE NumClusters 8 r_DS 2 5 SF_sigma 65 SF_lambda 45 LNS_ksi 3 KF_mu 7 KF_sigma 3 KF_lambda 12 DS_mu 7 39 DS_sigma 0 63 DS_lambda 40 AS_D_mu 1 AS_D_sigma 0 25 AS_D_lambda 15 PerClusterAS_D 6 AS_A_mu 1 7 AS_A_sigma 0 19 AS_A_lambda PerClusterAS_A 12 ll me o ES_D_mu 0 7 ES_D_sigma 0 2 ES_D_lambda 15 4 DS 3 pp 73 PerClusterES_D 3 ES_A_mu 0 95 ES_A_sigma 0 16 ES_A_lambda 15 Z DS 3 pp 72 PerClusterES_A 7 xpr_mu 8 4 xpr_sigma 4 Adjustments have been made to keep swcorr matriz positive definite asD_ds 0 4 asA_ds 0 7 0 8 asA_sf 0 5 asD_sf 0 4 0 5 Copyright Fraunhofer Heinrich Hertz Institute 52 eMail quadriga hhi fraunhofer de anana a N 60 61 62 63 64 65 66 67 68 69 70 atk wn O N INN IANN NNN wWwnr OO MAN 0 A QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE ds_sf 0 4 asD_asA 0 3 asD_kf 0 1 asA_kf 0 2 ds_kf 0 4 sf_kf 0 3 esD_ds 0 4 0 5 esD_asD 0 4 0 5 esA_sf 0 8 esA_asA 0 4 4 A logi0 d B C ltogi0 f D Togt hBS E 1logi0 RMS 4 Two different values first before breakpoint last after breakpoint 4 Different SF coeffi
106. ent contains the positions from the previous segment the second from the current This is needed to generate overlapping channel segments for the merging process Input i segment A list of indices indicating which subtracks should be returned By default all subtracks are returned Output subtracks A vector of track objects corresponding to the number of segments dist interpolate movement si method Description Interpolates the movement profile to a distance vector This function interpolates the movement profile The distance vector at the output can then be used to interpolate the channel coefficients to emulate varying speeds See also the tutorial Applying Varying Speeds Channel Interpolation in Section A 6 for more details Input si the sampling interval in seconds method selects the interpolation algorithm The default is cubic spline interpolation Op tional are e nearest Nearest neighbor interpolation e linear Linear interpolation e spline Cubic spline interpolation e pchip Piecewise Cubic Hermite Interpolating Polynomial e cubic Cubic spline interpolation Output dist Distance of each interpolated position from the start of the track in m interpolate_posit ions samples_per_meter Description Interpolates positions along the track This function interpolates the positions along the track such that it matches the samples per meter specifies in the simulation p
107. er of the multipath component Copyright Fraunhofer Heinrich Hertz Institute 70 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 2 5 Calculation of the Channel Coefficients Implemented in gt channel_builder get_channels and gt array interpolate Next we combine antenna patterns polarization and phases to calculate initial channel coefficients for each snapshot of a segment The antennas are defined by their polarimetric response F containing vertical and the horizontal polarization in spherical coordinates NKS 07 F 0 ae f 76 Since we already know the arrival and departure angles of each MPC we can combine the response from both the Tx and Rx antenna with the polarization rotation and get the coefficient pol F 6 gayt M F 7 0 77 Irt lm s Each MPC has a random initial phase w Hence by summing up the 20 sub paths in order to get one path per cluster we get a random cluster power This is compensated by normalization where we first sum up the complex phases and then average the power over all S snapshots of the segment We calculate the raw channel coefficients as aig exp jY m IVr t m s 78 e 1 20 2 norm A D L Wain 7 s m 1 20 few ff poll 4 Ir t 1 8 _ plnorm i De Ir t l m s Prt lms 80 r t l m 1 where P is the initial power assigned to each cluster 3 2 6 Path Gain Shadow Fading and K Factor Implemented in
108. erivation of the Correction Function The correction function Cy L K takes the influence of the K Factor and the varying number of clusters into account To approximate the function we generate the angles as described above Then we calculate the angular spread from the simulated data and compare the output of the procedure with the given value of og The angular spread og is calculated from the given P and as Rap02 _ LAP Fo Ge Qn 4 1 44 L Fr Peepi gien l 1 where is the angle calculated by 40 with the correction function set to C 1 Fn is the n th complex Fourier coefficient The correction function now follows from comparing og with og However two aspects need to be considered here 1 Due to the randomization of the angles in 39 we have to take the average angle over a sufficiently large quantity 1000 realizations of gg This values is denoted as a4 2 Due to the logarithm in 38 and the modulo operation in 40 there is a nonlinear dependency of the angular spread that can be found in the output data and the value given to the model However for small values the relationship can be approximated by a linear function We define this maximum angular spread oj of the linear approximation as the point where the error between the corrected value OL and is 10 For each L 2 42 and Kjagj 20 20 we now numerically calculate the value of C L K by 1 gmax _ Cin f r Tla ag
109. ertically Fy and horizontally Fp polarized component of the antenna response Peoh 216 8 o M is the 2 x 2 polarization coupling matrix This matrix describes how the polarization changes on the way from transmitter to receiver It is important to note that in a MIMO configuration which uses multiple antennas at transmitter and receiver the physical propagation effects such as the angles of departure and arrival the path powers the delays and the polarization coupling stay the same for all antennas The only difference is that each antenna element has a different field pattern Changing the orientation of antennas Here we describe how the orientation of an antenna can be changed e g when moving the receiver along a street or turning a mobile phone in the hands of the user This is done in two steps First the field patterns for both polarizations Fy and Fy are rotated and interpolated separately The second step then includes the effects of the polarization An antenna pattern 7 is given in spherical coordinates as a function of the azimuth angle and elevation angle 0 When the orientation of the antenna element changes the field pattern has to be read at different Dipole antenna 0 tilt Dipole antenna 20 tilt vertical pattern X Z X LL 20 15 10 0 Attenuation dB Figure 7 Example patterns for a dipole antenna Copyright Fraunhofer Heinrich Hertz Insti
110. ery computing intensive and may slow down the simulations significantly This parameter reduces the update rate A value of 0 2 Default Setting means that the polarization rotation is only updated when any of the four angles AoA EoA AoD EoD changes more than 0 2 Otherwise it is kept constant at its last value For highest precision calculations set drifting_update_threshold to 0 use_polarization_ rotation Select the polarization rotation method use_polarization_rotation 0 Uses the polarization method from WINNER No polarization rotation is calculated use_polarization_rotation 1 Uses the new polarization rotation method where the cross polarization ratio XPR is modeled by a rotation matrix No change of circular polarization is assumed use_polarization_rotation 2 Default Uses the polarization rotation with an additional phase offset between the H and V component of the NLOS paths The offset angle is calculated to match the XPR for circular polarization use_polarization_rotation 3 Uses polarization rotation for the geometric polarization but models the NLOS polarization change as in WINNER use_absolute_delays Returns absolute delays in channel impulse response CIR By default delays are calculated such that the LOS delay is normalized to 0 By setting use_absolute_delays to 1 or true the absolute path delays are included in channel delays at the output of the model
111. etween the segments For example in between 7 5 and 10m the DS drops from 0 33 to 0 12 microseconds Additional fluctuations are caused by small scale fading coeff squeeze c coeff delay c delay pow_tap abs coeff 72 pow_sum sum pow_tap mean_delay sum pow_tap delay pow_sum ds sqrt sum pow_tap delay 2 pow_sum mean_delay 2 figure plot dist ds 1e6 title Simulated Delay Spread xlabel Distance from start point m ylabel RMS DS mus axis 0 20 0 1 grid on Simulated Delay Spread m a a o 7 7 T 7 1 i i gt 9 RMS DS is n 2 p h ob P 7 7 7 i 1 i 2 7 f 0 i i 0 5 10 15 20 Distance from start point m Figure 40 DS along the track manual parameter selection close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 Copyright Fraunhofer Heinrich Hertz Institute 112 eMail quadriga hhi fraunhofer de
112. evolving sequences out if the snipes produced by the channel builder Addi tional function such as the transformation into frequency domain can help the user to further process the data An overview of the model software is depicted in Fig 3 The unified modeling language UML class diagram of the QuaDRiGa channel model gives an overview of all the classes methods and properties of the model The class diagram serves as a reference for the following descriptions which also lists the methods that implement a specific functionality Copyright Fraunhofer Heinrich Hertz Institute 19 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE name interpolation_method no_elements lt lt input gt gt simulation_parameters sample density samples_per_meter drifting precision t drifting update threshold t use_polarization_rotation use absolute delays t use_ subpath_output show_progress bars center_frequency map_resolution apply_common_phase generate copy_element copy_objects estimate _common_phase estimate pol vector import_pattern interpolate rotate_pattern set_grid visualize set_ speed create parameter _sets generate generate parameters get_channels power_map randomize rx_positions set_pairing set_satallite pos visualize Implements the state merger and the functions to interpolate a specific s
113. f the number of clusters is different in both segments clusters are ramped up or down without a counterpart from the new old segment The ramp is then stretched over the whole overlapping area For the LOS path we continuously adjust power and phase over the overlapping area since it has the same delay in both segments In order to minimize the impact of the transition on the instantaneous values of the LSPs paths need to be carefully matched For example if a path with a small delay ramps down and a similarly strong path with a longer delay ramps up then the DS increases This increase or decrease can fluctuate randomly along the merging interval To balance it out we pair paths from both segments that minimize the fluctuations This is done by first determining the values of the DS before and after the transition Then we calculate a Copyright Fraunhofer Heinrich Hertz Institute 72 eMail quadriga hbhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Transitions between Segments overlapping part d E N 50 8 000000000002 merging area initial pos fo variable length segment 2 inital pos Postprocessing Variable Speeds gt segment 1 original snapshots E Po r constant ae we XP SoM Or interpolated snapshot ed constant samplerate wo Figure 15 Top Illustration of the overlapping area used for calculating the transitions between segments step G Bottom Illustration of the inte
114. ff 1 1 1 72 los_pwr_nodrift 10 1log10 squeeze abs dn 1 coeff 1 1 1 72 figure plot degrees los_pwr_drift hold on plot degrees los_pwr_nodrift r hold off a axis axis 0 360 a 3 4 xlabel Position on circle degree ylabel Power of the LOS component title Power of the LOS component for the circular track legend Drifting No drifting 4 Copyright Fraunhofer Heinrich Hertz Institute 95 eMail quadriga hhi fraunhofer de N He QuaDRiGa v1 2 3 307 A TUTORIALS Power of the LOS component for the circular track PDP for the circular track with drifting 85 r i 90 90 i AU al Mt 95 3 Wid ee oN aie 2 Hie As Ad hh S 95 Vi Ng iy D 100 g KA hc iy 5 Hg il ii 2 gt 100 Wh yi W g ios a iN iy ll 5 P K Wi i Z 110 2 105 il ii i J g 4 5 l i 225 E ia g 4 3 110 4 E i E 120 E 4 115f a i 2 i 125 i Drifting No drifting i f 30 120 i i ERN 0 50 100 150 200 250 300 350 0 032 064 0 96 1 28 16 1 92 22 Position on circle A Delay us Figure 29 Received power on the circular track time evolution tutorial When drifting is enabled Fig 29 left blue curve the channel output after merging is time continuous The variations along the track come from the drifting K Factor and the dri
115. first three taps from both the original and the interpolated channel plotted on top of each other The values are identical except for the fact that the interpolated values blue line have 5 times as many sample points Copyright Fraunhofer Heinrich Hertz Institute 99 eMail quadriga hhi fraunhofer de e WNE QuaDRiGa v1 2 3 307 A TUTORIALS Tx Position 407 7 A Tx Antenna O Rx Position 20F Y Position gt Ed i i i 1 i 0 10 20 30 40 50 60 70 80 X Position Figure 31 Scenario setup for the speed profile tutorial pwr_orig 10 1log10 squeeze abs cn coeff 1 1 1 3 72 nsnap cn no_snap dist_orig O nsnap 1 t get_length nsnap 1 pwr_int 10 lo0og10 squeeze abs ci coeff 1 1 1 3 72 figure plot dist_orig pwr_orig r Linewidth 2 hold on plot dist pwr_int b hold off axis min dist max dist min pwr_orig pwr_orig gt Inf max pwr_orig pwr_orig gt Inf 10 xlabel Distance from start point m ylabel Power dB Fig 32 right shows the power delay profile PDP for the interpolated channel As defined in the track object it starts with a LOS segment going into a shaded area with significantly more multipath fading at around 4 seconds and then back to LOS at around 13 sec 80 2 5 85 o0 it 7 57 i 95 2 10 4 100 5 12 5 J
116. fting shadow fading When drifting is disabled these parameters are not updated and kept fixed at their initial value At the end of each segment both channels are cross faded I e the power of the output of the first segment ramps down and the power of the second segment ramps up Since drifting guarantees a time continuous evolution of the phase this ramping process is also time continuous and no artifacts are visible in the blue curve Without drifting however the phases are approximated based on their initial values the initial arrival and departure angles and the traveled distance from the start point However since the Rx moves along a circular track the angles change continuously which is not correctly modeled The phase at the end of the first segment does not match the phase at the beginning of the second When adding both components artifacts appear as can be seen in the red curve Next we plot the power delay profiles for both tracks We calculate the frequency response of the channel and transform it back to the time domain by an IFFT Then we create a 2D image of the received power at each position of the track We start with the circular track h cn 1 fr 100 e6 512 h squeeze h pdp 10 logi0 abs ifft h 1 72 figure imagesc pdp 1 256 caxis max max pdp 50 max max pdp 5 J colorbar cm colormap hot colormap cm end 1 1 set gca XTick 1 32 255 set gca
117. generation First we parameterize the channel model We start with the basic simulation parameters For the desired output we need two additional options we want to evaluate absolute delays and we need to get all 20 subpaths Normally the subpaths are added already in the channel builder s simulation_parameters s center_frequency 2 53e9 s sample_density 4 s use_subpath_output 1 s use_absolute_delays 1 Second we define a user track Here we choose a linear track with a length of 30 m The track start 20 m east of the transmitter and runs in east direction thus linearly increasing the distance from the receiver 1 layout s 1 tx_position 3 25 l track generate linear 30 0 l track initial_position 20 0 0 1 track scenario WINNER_UMa_C2_L0S l track interpolate_positions s samples_per_meter 1l visualize Tx Position 257 J A gt Tx Antenna O Rx Position 20L W Rx Antenna Rx Track g g Z OfFx1 oM_ WINNER UMa 2 LOoS _ 4 gt 5b 4 10 4 15 4 20 4 25 4 L L L L L L 0 10 20 30 40 50 X Position Figure 24 Scenario setup for the drifting phases tutorial Now we generate the LSPs In order to get repeatable results we set a specific random seed This is a MATLAB internal function and is not a feature of the channel model We also set the shadow fading and K factor to 1 and disable the path loss model
118. h the default settings call set_scenario scenario Input scenario A cell array of scenario names Each scenario synonym for propagation en vironment is described by a string e g MIMOSA _16 25_LOS or WIN NER_SMa_C1_NLOS A list of supported scenarios can be obtained by calling parameter_set supported_scenarios The scenario parameters are stored in the configuration folder config in the QuaDRiGa main folder The filenames e g MIMOSA_16 25_LOS conf also serves as scenario name probability The probability for which the scenario occurs This parameter must be a vector of the same length as there are scenarios Probabilities must be specified in between 0 and 1 The sum of the probabilities must be 1 By default or when probability is set to each scenario is equally likely seg_length_min seg_length_mu seg_length_std the minimal segment length in m The default is 10 m the median segment length in m The default is 30 m the standard deviation of the street length in m The default is 12 m set_speed speed Description Sets a constant speed in m s for the entire track This function fills the track movement_profile field with a constant speed value This helps to reduce computational overhead since it is possible to reduce the computation time by interpolating the channel coefficients Input speed The terminal speed in m s split_
119. he map generation ds_kf Correlation of delay spread and K Factor asA_ds Correlation of delay spread and azimuth of arrival angle spread esA_ds Correlation of delay spread and elevation of arrival angle spread ds_sf Correlation of delay spread and shadow fading asA_kf Correlation of K Factor and azimuth of arrival angle spread esA_kf Correlation of K Factor and elevation of arrival angle spread sf_kf Correlation of K Factor and shadow fading esA_asA Correlation elevation of arrival angle spread and azimuth of arrival angle spread asA_sf Correlation of shadow fading and azimuth of arrival angle spread esA_sf Correlation of shadow fading and elevation of arrival angle spread Cluster Parameter Those parameters influence the generation of the scattering clusters and the dis tribution of the sub paths within each cluster NumClusters Integer The number of clusters generated For multipath rich environments typically more clusters are used If the LOS component is dominant a lower number of clusters is sufficient PerCluster deg The azimuth angular spread of the 20 sub paths within one cluster AS_A PerCluster deg The elevation angular spread of the 20 sub paths within one cluster ES_A LOS scatter_radius meter This parameter allows an additional spread of the 20 sub paths of the LOS compo nent by emulating scattering in the near field of the antennas EXPERIMENTAL LNS_ksi Normally cluster powers are taken from an exponential
120. he path lengths the terminal will also see a change in its Doppler profile 5 Stopping at traffic light LOS QuaDRiGa performs all internal calculations at a constant speed However a stop of the car at a traffic light is realized by interpolating the channel coefficients in an additional post processing step step 4 Here the user needs to supply a movement profile that defines all acceleration deceleration or stopping points along the track An example is given in section A 6 Since the interpolation is an independent step it makes no difference if the mobile terminal is in LOS or NLOS conditions 6 Turning off with change of reception condition LOS NLOS This is realized by combining the methods of point 2 scenario change and point 4 turning without change The scenario change is directly in the curve Thus the LOS and the NLOS segments have an overlapping part where the cluster powers of the LOS segment ramp down and the NLOS clusters ramp up The update of the angles delays and phases is done for both segments in parallel 7 Crossing side Street NLOS short LOS gt NLOS This is modeled by two successive scenario changes NLOS LOS and LOS NLOS For both changes a new set of clusters is generated However since the parameters for the two NLOS segments are extracted from the same map they will be highly correlated Thus the two NLOS segments will have similar properties 8 Structural change in the environment with
121. hofer Heinrich Hertz Institute 61 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 2 Calculation of Channel Coefficients In the model software the channel coefficients are generated by the channel builder It takes the correlated large scale parameter values as input and calculates the CIR for each user position Each scattering cluster is represented by a propagation path which is modeled as a Dirac function in delay domain A path is made of up of 20 spatially separated subpaths according to the sum of sinusoids method PBO1 Path powers path delays and angular properties for both sides of the link are modeled as random variables defined through probability density functions PDFs and cross correlations All parameters except the fast fading are drawn independently in time in what is termed drops see 3GP05 Even though a path consists of multiple subpaths in an angular domain it remains a single tap in delay domain 3 2 1 Initial Delays and Cluster Powers Implemented in channel_builder generate_initial_paths Initial delays are drawn randomly from a scenario dependent delay distribution as rl r o In X 31 where X uni 0 1 is a uniformly distributed random variable having values in between 0 and 1 is the initial DS from the map and r is a proportionality factor r has been introduced in 3GP11 because a is influenced by both the delays 7 and the powers P r is usua
122. i e no drifting spherical waves time evolution multipath fading etc This function can thus be used to acquire quick previews of the propagation conditions for a given layout Input h_parset A parameter_set object see Section 2 2 5 Output h_channel A channel object see Section 2 2 7 The output contains one coefficient for each position in h_parset position Copyright Fraunhofer Heinrich Hertz Institute 43 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 7 Class channel Objects of this class are the output of the channel model They are created by the channel_builder By default channel coefficients are provided in time domain as a list of delays and complex valued amplitudes However this class also implements certain methods to postprocess the channel data Those include e Transformation into frequency domain e Interpolation in time domain to change the terminal speed and sampling rate e Combining channel traces into longer segments including birth and death of clusters Properties name Name of the channel object This string is a unique identifier of the channel object The channel_builder creates one channel object for each MT each Tx and each segment They are further grouped by scenarios propagation environments The string consists of four parts separated by an underscore Those are e The scenari
123. iGa channel model is depicted in Fig 4 This figure shows how each of the processing steps which are described in detail in the following sections are linked together The lines show which parameters are exchanged and how often they are updated Black lines are for parameters that are either provided by the model users or which are given in the parameter table Those values are constant Blue values are updated once per segment and red values are updated once per snapshot User Input Variables Terminal trajectories Network layout Propagation scenarios Transmitter positions Antenna Parameters Speed profile Carrier frequency Trajectories 7 Scenarios Transmitter Positions Split terminal trajectories into segments Snapshot position Antenna patterns and array geometries F LOS direction Los Scenarios Snapshot position t Path Loss Parameters XPRu XPRo No clusters L Parameter Table Cluster wise azimuth spread Caoa Calculate path loss for each snapshot Drifting Path Loss Snapshot position Interpolate KF and SF maps Draw random initial phases Generate XPR Generate channel coefficients X pol power ratios Drifting angles of arrival Generate drifting delays Overlapping area Connect successive Scaled channel Apply path loss K factor channel traces coefficients and shadow fading Merged channel coefficients Generation of Correlated Large Scale Parameters Per cluster SF std
124. igure 9 Principle of the map generation statistics e g a second terminal right next to the current user will experience a similar DS The second granularity of the large scale parameters are thus the specific values of the LSPs for each user in the scenario Generating those values can be seen as going backwards from the distribution 4 0 of a parameter to individual measurement values At the example position in the map the DS is 53 ns Path Level Last the individual components of the CIR are calculated This procedure takes the values of the LSPs into account and calculates the power and the delay of the channel coefficients The correlation maps are generated at a fixed sampling grid by successively filtering a random normal distributed sequence of numbers with a finite impulse response FIR filter The principle is depicted in Fig 9 The map is represented by a matrix B and one pixel of that matrix is By where y is the row index and x is the column index The first pixel B1 is in the top left or north west corner of the map The FIR filter coefficients are calculated from the decorrelation distance d in units of m The distance dependent correlation coefficient follows an exponential function p d e 22 with d as the distance between two positions Gud91 We now calculate two sets of filter coefficients one for the horizontal and vertical directions and one for the diagonal elements This is done by taking 22 and subs
125. imuth angle ranging from 180 to 180 degrees The third dimension belongs to the element number The default resolution is 1 degree Hence the default size of fVi is j181x361x1j If a different resolution is given the optional variables azimuth_grid and elevation_grid must be defined The field pattern s for the horizontal polarization given in spherical coordinates fHi can be empty if no horizontal response is given If it is given then fHi must have the same size as f Vi It is possible to remove a common phase offset for both vertical and horizontal polarization This offset can occur when an antenna was characterized in an ane choic chamber where the antenna was not placed exactly in the center Hence there is a phase difference for each angle Default setting 0 No correction The required accuracy i e the target mean square error MSE of the approxi mation The MSE is defined as N abs fVi 72 abs fHi 72 offset abs fVi fVo 72 abs fHi fHo 72 MSE mean offset N This variable is tracked throughout the converting process If the approximation is good enough i e the MSE lt accuracy then the algorithm stops and returns the output pattern Default value le 6 This value limits the number of elements per field pattern The default value is 4 which allows a perfect approximation However more elements require more computing time in the channel model
126. ion Position of each Tx in global cartesian coordinates using units of m rx_position The receiver position global cartesian coordinates using units of m for each snapshot no_rx Number of receive elements read only no_tx Number of transmit elements read only no_path Number of paths read only no_snap Number of snapshots read only Methods h_channel channel Ccoeff Cdelay Cinitial_position Description Creates a new channel object Input Ccoeff The complex valued channel coefficients for each path Cdelay The delays for each path Cinitial_position The snapshot number for which the initial LSPs have been generated Output h_channel A channel object Copyright Fraunhofer Heinrich Hertz Institute 44 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE freq_response fr bandwidth carriers isnapshot Description Input Transforms the channel into frequency domain and returns the frequency response bandwidth carriers i snapshot The baseband bandwidth in Hz The carrier positions There are two options 1 Specify the total number of carriers In this case carriers a scalar natural number gt 0 The carriers are then equally spaced over the bandwidth 2 Specify the pilot positions In this case carriers is a vector of carrier positions The carrier positions are given relative to the
127. is only holds when the distance to the LBS is large Here the initial distance is small ca 5 m When the initial angles are kept fixed along the track the error is significant Here the phase ramp is negative indicating a movement direction towards the scatterer and thus a higher Doppler frequency However when the scatterer is passed the Rx moves away from the scatterer and the Doppler frequency becomes lower This is not reflected when drifting is turned off Note that with shorter delay spreads as e g in satellite channels the scatterers are placed closer to the Rxs initial position This will amplify this effect Hence for correct time evolution results drifting needs to be turned on Copyright Fraunhofer Heinrich Hertz Institute 92 eMail quadriga hhi fraunhofer de wd QuaDRiGa v1 2 3 307 A TUTORIALS pow abs squeeze sum d coeff 1 1 2 5 72 plot distance 10 logi0 pow r xlabel Distance from track start point ylabel Tap power dB close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 100 0 i 0 4 Q 5t 100 SS S 1 l S 200 SSS 10 2 SS 2 5 300F E 1 g 15t 5 S J 2 g 400 F SS J 5 E 500 Ss e 8 E 600F 5 25L 700 NS 30F 800 900 i fi fi fi fi 35 fi fi L fi fi 20 25 3
128. itions The WINNER model only allows constant speeds via a continuous rotation of the phases of the MPCs However realistic scenarios would also include accelerations decelerations and MTs with different speeds e g pedestrian and vehicular users However to minimize the computational overhead and memory require ments we generate the channel coefficients at a constant sample rate which fulfills the sampling theorem i e updates are triggered at least twice per half wave length A time series for varying speeds is then obtained by interpolating the coefficients in a separate postprocessing step Input variables A Calculation of B Calculation of C Calculation of network layout y correlated large scale gt initial delays and gt departure and NN terminal trajectories parameter maps cluster powers arrival angles D Drifting of the initial propagation scenario delays angles and phases antenna patterns over a short segment of G Tadi F Application of E Calculation of the terminal trajectory Postprocessing Analysis x EET path gain shadow polarized channel a between Segments fading and K Factor coefficients Figure 5 Essential steps for the calculation of time evolving channel coefficients 3 1 Tracks Scenarios Antennas and Network Layout 3 1 1 Drops and Segments The concept of segments and drops is already described in KMH 07 See also Fig 6 Chan
129. ividual lines are for different numbers of paths ranging from 3 lowest line to 42 top line Right Comparison of the angular spread og set by the parametrization and the angular spread in the data Table 12 Maximum Linear Angular Spread vs K Factor K Az of EL og K Az of E o K Az og Eleg 20 206 201 6 211 209 8 155 151 18 207 201 4 209 206 10 143 135 16 207 202 2 209 205 12 128 118 14 205 202 0 200 198 14 113 101 12 208 203 2 192 190 16 99 84 10 212 204 4 182 179 18 86 70 8 213 208 6 170 166 20 74 57 Table 13 Values of C L K K L 3 6 9 12 15 18 21 24 27 30 33 36 39 42 20 3 59 5 53 6 25 6 62 6 81 6 93 7 04 7 11 7 17 7 13 7 22 7 22 7 25 7 31 18 3 76 5 63 6 34 6 71 6 78 6 98 7 05 7 07 7 19 7 18 7 23 7 21 7 28 7 26 16 4 00 5 76 6 36 6 74 6 88 6 96 7 08 7 09 7 18 7 14 7 18 7 24 7 26 7 28 14 4 24 5 90 6 51 6 78 6 89 6 92 6 99 7 05 7 08 7 16 7 14 7 15 7 06 7 10 12 4 54 5 98 6 47 6 68 6 74 6 84 6 86 6 92 6 86 7 03 7 38 7 80 8 36 8 77 10 4 74 5 98 6 38 6 51 6 55 6 66 7 07 7 76 8 54 9 03 9 65 10 12 10 55 10 97 8 4 83 5 80 5 99 6 27 7 27 8 38 9 12 9 89 10 45 10 90 11 44 11 74 12 10 12 35 6 4 70 5 30 6 42 7 96 9 13 10 01 10 69 11 36 11 78 12 15 12 60 13 01 13 15 13 47 4 4 12 5 72 8 00 9 42 10 34 11 09 11 52 12 16 12 57 12 86 13 20 13 39 13 72 14 02 2 3 42 6 94 888 10 01 10 71 11 32 11 90 12 15 1265 12 95 13 17 13 387 13 53 13 75 0 4 19 7 69 9 07 10 05 10 68 11 09 11 58
130. l Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 81 QuaDRiGa v1 2 3 307 A TUTORIALS A 3 Generation of Satellite Channels This script demonstrates the parametrization of the channel model to generate time continuous sequences for a satellite scenario Setting up the Simulation Parameters First we set up the general simulation parameters We choose a center frequency of 2 1 GHz We also want to use drifting in order to get the correct delays and angles for the time continuous simulation A sample density of 2 5 ensures that the channel coefficients can be interpolated to different playback speeds later on close all clear all s simulation_parameters 4 Basic simulation parameters s center_frequency 2 185e9 s sample_density 0 25 RandStream setGlobalStream RandStream mti9937ar seed 1 Creating a random Track and defining states along the track Next we generate a simulation track A track describes the movement of a mobile terminal It is composed of an ordered list of positions During the simulation one snapshot is generated for each position on the track Later on the generation of the track is done by the state sequence generator Here we implement a simple version of the sequence generator to generate a random track We first create a set of streets with different length We assume a normal distribution of the street length where the parameters mu and sig
131. larization direction can be changed at the boundary surface between two dielectric media T Svantesson Sva01 provided a method for modeling the polarization of a reflected wave where the polarization coupling is a function of several geometric parameters such as the orientation of the scatterers However these parameters are not generally available in the SCM In addition to that only metallic reflec tions keep the polarization unchanged Reflections at dielectric media can cause changes of the polarization being a function of the complex valued dielectric constant of the media and of the angle of incidence Hence not only the polarization angle might change but also the polarization type In order to address this issue Copyright Fraunhofer Heinrich Hertz Institute 69 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION studies of the polarizations effects in individual scattering clusters in several outdoor and indoor scenarios were done MTIO09 QOHDD10 PHL 11 The published results indicate that in many cases scattering preserves the polarization quiet well However since only the powers of the elements in the polarization coupling matrix were analyzed no conclusions can be drawn on how elliptic the polarization of the scattered wave becomes We assume that the polarization coupling matrix M for the NLOS components can be described by a combination of linear transformations Hence we can take advan
132. lation_method property of the array object to linear Remark There are additional input parameters specified in the mat File that are not in the list below Those parameters correspond to the properties of the array class Passing those variables during the function call takes less time than reading them from the object properties This is used internally in channel_builder get_channels but is irrelevant here Input azimuth A vector of azimuth angles in rad elevation A vector of elevation angles in rad element The element numbers for which this interpolation is done is applied If no element number is given the interpolation is done for all elements in the array Output V The interpolated vertical field pattern H The interpolated horizontal field pattern CP The interpolated common phase field pattern dist The effective distances between the antenna elements when seen from the direction of the incident path The distance is calculated by an projection of the array positions on the normale plane of the incident path cp rotate_pattern deg rotaxis element usage Description Rotates antenna patterns Pattern rotation provides the option to assemble antenna arrays out of single elements By setting the element_ position property of an array object elements can be placed at different coordinates In order to freely design arbitrary array configurations however elements often need to be rotate
133. length Segment length ind ind segment_length 4 Start of next segment if ind lt t no_snapshots 4 Exception for the last segment t no_segments t no_segments 1 t segment_index t no_segments ind end end Track layout Track Oo Segment start 250 4 200 7 E 150 e 3 3 3 sh IMOSA_10 45_LOS 1007 3 IMOSA_10 45_ OS SMIMOSA_10 45_NLOS 50 MIMOSA_10 45_LOS oL MOSA_10 45_LOS 250 200 150 100 50 0 X direction m Figure 21 Receiver track for the satellite channel tutorial Copyright Fraunhofer Heinrich Hertz Institute 83 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Finally we interpolate the track to the given sample density 2 samples per half wave length and plot the track see Fig 21 t interpolate_positions s samples_per_meter t visualize Defining Antenna Arrays In the third step we set up our antenna arrays for the transmitter at the satellite and the receiver We use synthetic dipole antennas for this case Two dipoles are crossed by an angle of 90 degree The signal is then split and fed with a 90 degree phase shift to both elements generating RHCP and LHCP signals 4 Create a patch antenna with 120 degree opening a array custom 120 120 0 4 Copy element 1 to element 2 the resulting antenna array has two 4 elements both dipoles a copy_element 1 2 Z Rotate the second pattern by 90 degree around the z axis a rot
134. lly calculated from measurement data Next the delays are normalized such that the first delay is zero and then they are sorted in descending order r sort fa min a 32 The NLOS cluster powers are drawn from a single slope exponential power delay profile PDP depending on the DS c and and a random component Z N 0 is a scenario dependent coefficient emulating an additional shadowing process among the clusters in one segment It is normally obtained from measurements po 1 a PL exp 7 100 33 T07 The power of the first cluster is further scaled according to the initial KF from the map and cluster powers are normalized so that their sum power is unity L L p zR ye R a re 34 l 2 l 1 In the last step we correct the influence of the KF on the DS which has changed due to the scaling The DS after applying 34 is calculated by L L 2 6 Refn 7 a 35 1 1 I 1 The cluster delays are then obtained by with o being the initial DS from the map Copyright Fraunhofer Heinrich Hertz Institute 62 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 2 2 Departure and Arrival Angles Implemented in channel_builder generate_initial_angles and channel _builder correction function We calculate four angles for each cluster the azimuth of departure AoD the elevation of departure EoD 6 the azimuth of arrival AoA and the ele
135. ma were fitted from random distances between two crossings in central Berlin measured with Google earth street_length_mu 187 4 Average street length in m street_length_sigma 83 min_street_length 50 turn_probability 0 4 4 The prob that the car turns at a crossing curve_radius 10 4 The curve radius in m diro rand 2 pi 4 Random start direction For the given parameters we calculate a list of points along the track that resemble the street grid and the turns at crossings point 0 4 The start point always at 0 0 m 1 4 A counter for the points for n 1 3 4 We simulate 3 street segments Get a random street length drawn from the distribution defined above street_length randn street_length_sigma street_length_mu while street_length lt min_street_length street_length randn street_length_sigma street_length_mu end 4 Get 3 points along the street point m 1 point m exp 1j diro street_length 0 1 point m 2 point m exp 1j diro street_length 0 9 point m 3 point m exp 1j diro street_length m m 3 4 At a crossing the car could change its direction This is 4 modeled here if rand lt turn_probability dirn diro sign rand 0 5 pi 2 randn pi 12 point m 1 point m curve_radius exp 1ij diro exp 1j dirn diro dirn m m 1 end end Copyright Fraunhofer Heinrich Hertz Institute 82 eMail quadriga hhi fraunhofer de
136. means that when the terminal moves 87 m in any given direction then the correlation of the value at this distance with the value at the initial position is e 0 37 e Delay Spread DS The root mean square RMS delay spread is probably the most important single measure for the delay time extent of a multipath radio channel The RMS delay spread is the square root of the second central moment of the power delay profile and is defined to be Ne hee Lo i 2 e n fe A gt xP a e l 1 l 1 with P is the total received power P the cluster power and 7 the cluster delay In order to generate the coefficients QuaDRiGa has to generate delays for each of the multipath clusters I e the total lengths of scattered paths have to be defined This generation of delays is governed by value of the DS in a specific environment The DS is assumed to be log normal distributed and defined by two parameters Its median value u and its STD Thus a values of DS of 6 69 corresponds to 204 ns o then defines the range of possible values E g DS 0 3 leads to typical values in the range of 1076 69 0 3 102 ns to 1076 69 0 3 407 ns As for the shadow fading the decorrelation distance DS defines how fast the DS varies when the terminal moves through the environment The delay spread is calculated from both the delays 7 and the path powers P Le lager delay spreads g can either be achieved by increasing the values of
137. ment 1 6 5 0 splits all segment longer than 6 m into subsegments of 5 m length Each segment gets assigned a scenario This is also essential since many parameters such as the number of clusters the XPR etc are scenario specific Hence they are the same for the entire scenario Here we set the first the segments to NLOS the third to LOS and the last to NLOS Last we set a random starting position for the track in the layout 1 tx_position 0 0 25 4 Set Tx position t track linear 20 4 Linear track 20 m length t interpolate_positions s samples_per_meter Interpolate to sample density t split_segment 1 6 5 0 4 Split in 4 segments Un WINNER_UMa_C2_NLOS Ul WINNER_UMa_C2_L08S t scenario Un Un U1 Un 4 Set scenarios l randomize_rx_positions 500 0 0 0 Random start position tmp 1 rx_position l track t 1l rx_position tmp 1l visualize Manual setting of the parameters Now we initialize the parameter set objects The method 1 create_parameter_sets splits the track into smaller sub tracks one for each segment It further extracts the scenario informations Each scenario gets its own parameter set object So we get an Copyright Fraunhofer Heinrich Hertz Institute 109 eMail quadriga hhi fraunhofer de a fF wn wn QuaDRiGa v1 2 3 307 A TUTORIALS 250 J F Be Position A Tx Antenna 200 4 i O Rx Position Y Rx Antenna
138. ments in the array copy_element source target Description Creates a copy of an antenna element Input source Index of the array object that should be copied The value must be scalar integer and greater than 0 and it can not exceed the array size target Target can be a scalar or vector with elements gt 0 copy_objects Description A modified version of the standard physical copy function While the standard copy command creates new physical objects for each element of obj in case obj is an array of object handles copy_objects checks whether there are object handles pointing to the same object and keeps this information estimate_common_phase element Description Estimates the common phase from the field patterns It is possible that antenna patterns have a phase component For example an antenna array might be assembled out of several elements which are at different position in the array When the patterns of the elements are then calibrated in the lab the individual positions result in a phase offset which is part of the measured pattern response The core function of QuaDRiGa however only uses the real part if the provided patterns Hence not calibrating the phase offset out of the pattern will lead to errors This function calculates the phase from the pattern and stores it in the common_phase property of the antenna array obj
139. n the positions and stored the output in the ground_direction and height_direction field of the track object copy_objects Description A modified version of the standard physical copy function While the standard copy command creates new physical objects for each element of obj in case obj is an array of object handles copy_objects checks whether there are object handles pointing to the same object and keeps this information correct_overlap overlap Description Corrects positions of the segment start to account for the overlap After the channel coefficients are calculated adjacent segments can be merged into a time continuous output The merger assumes that the merging interval happens at the end of one segment before a new segments starts In a reality however the scenario change happens in the middle of the overlapping part and not at the end of it This function corrects the position of the segment start to account for that Input overlap The length of the overlapping part relative to the segment length It can have values in between 0 no overlap and 1 ramp along the entire segment generate linear track_length direction Description Creates a linear track with given length and direction Direction describes the travel direction along the track in rad in mathematical sense i e 0 means east pi 2 means north pi means west and pi 2 south If tra
140. na element at snapshot s ar l m s Al m 0 r s 49 Now we can obtain an update of the arrival angles by transforming a jm back to spherical coordinates Orima arctan r 1 m s y3 Grlm s e 50 Cin arcsin Petes 51 K Gretna We assume a static scattering environment Thus the departure angles at the Tx do not change We also assume that the distance between the Tx and the first scatterer is large compared to the Tx array dimension Hence we use the same departure angles for all Tx elements The phases and path delays however depend on the total path length d 1m To obtain this value we calculate the vector biz m o from the vectors r s and aims at r s 1 Dilimo Tig aims 52 drt lm s bi t m 0 Bema 53 Finally we calculate the phase Y and path delays rT 27 Urt lm s drt lm s mod A 54 1 20 Trtls 20 c Py drt ees 55 LOS drifting The direct component is handled differently since there are no discrete scatterers LOS fading however can occur if there are objects in the first Fresnel zone of the propagation path To simplify the calculations we assume that those objects have a constant distance to the Rx and are only separated by their angular distribution They are placed on a circle with radius dios as depicted in Fig 12 bottom We update the departure and arrival angles based on the vector r which points from the location of the Tx element t to the location of the Rx element r
141. nding fields can be left empty They will be completed by the parameter sets See also the method track generate_parameters on how to fill this structure automatically ground_direction Azimuth orientation of the terminal antenna for each snapshot This variable can be calculated automatically from the positions by the function track compute_directions Copyright Fraunhofer Heinrich Hertz Institute 30 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE height_direction Elevation orientation of the terminal antenna for each snapshot closed Indicates that the track is a closed curve Methods turn_probability h_track track track_type track_length direction street_length_min street_length_mu street_length std curve_radius Description Creates a new track object See track generate for a description of the input parameters and the list of supported track types compute_directions Description Calculates ground and height orientations from positions This function calculates the orientations of the transmitter based on the positions If we assume that the receive antenna array is fixed on a car and the car moves along the track then the antenna turns with the car when the car is changing direction This needs to be accounted for when generating the channel coefficients This function calculates the orientation based o
142. nel coefficients of adjacent segments The active time of a scattering cluster is confined within the combined length of two adjacent segments The power of clusters from the old segment is ramped down and the power of new clusters is ramped up within the overlapping region of the two segments The combination clusters to ramp up and down is modeled by a statistical process Due to this approach there are no sudden changes in the LSPs For example if the delay spread in the first segment is 400 ns and in the second it is 200 ns then in the overlapping region the delay spread DS slowly decreases till it reaches 200 ns However this requires a careful setup of the segments along the used trajectory If the segments are too short sudden changes cannot be excluded This process is described in detail in Section 3 2 7 1 5 QuaDRiGa Program Flow For a propagation environment e g urban suburban rural or tree shadowing typical channel characteris tics are described by statistics of the LSPs Those are the median and the standard deviation of the delay spread angular spreads shadow fading Ricean K Factor as well as correlations between them Additional parameters describe how fast certain properties of the channel change i e the decorrelation distance Those parameters are stored in configuration files which can be edited by the model user Normally the parameters are extracted from channel measurements A detailed description of the model step
143. nel segments represent a period of quasi stationarity during which probability distributions of low level parameters are not changed noticeably During this period all large scale parameters are practically constant To be physically feasible the channel segment must be confined in distance The size of the segments depends on the environment but it can be at maximum a few dozen meters The decorrelation distances of different parameters describe roughly the proper size of the channel segment These decorrelation distances can be extracted from measurements and are scenario dependent In the WINNER terminology a drop is defined as a segment with zero length In order to extend the length of a drop short term time variability of some channel parameters is added within the drops This method is known as drifting and was first described in BHS05 The current model uses drifting to enable time continuous simulations Here two types of drifting need to distinguished 1 Drifting of Path Delays and Angles and Polarization It is assumed that the positions of scatterers are fixed during a drop As a consequence the angle of departures AoDs of the scatters as seen from the BS do not change with the exception of the LOS AoD However based on the fixed geometry assumption the angle of arrivals AoAs of the scatters Copyright Fraunhofer Heinrich Hertz Institute 54 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION
144. nt correlation of the LSP If e g two mobile terminals in the above example are 40 m apart of each other their DS is correlated with a correlation coefficient of e 0 37 Additionally all parameters are cross correlated A typical example is the dependance of the angular spread e g the azimuth spread of arrival on the Ricean K factor KF With a large KF e g 10 dB a significant amount of energy comes from a single direction Thus the angular spread gets smaller which leads to a negative correlation between the DS and the KF Link Level When a user terminal is placed in a scenario black dot on the map in Fig 8 it experiences a radio channel which is determined by the specific values of the seven parameters at this position Due to the autocorrelation properties small distances between users also lead to high correlations in the channel Scenario Dependent Distribution Correlation Map Individual MT position RMS DS 53 ns Log Normal distributed RMS DS median 128 ns Individual Path Path Powers B05 2 0 4 0 100 200 300 400 500 E 0 3 RMS DS ns 30 2 E 0 1 T 2 9 0 100 200 300 400 500 delay ns Figure 8 Principle of the generation of correlated LSPs Copyright Fraunhofer Heinrich Hertz Institute 59 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Filter Filter a b 20 40 60 80 100 120 20 40 60 80 100 1290 20 40 60 80 100 120 60 80 100 120 20 40 60 80 10 n0 F
145. o manually set LSPs in QuaDRiGa This tutorial explains how to generate a time series of channel coefficients with manual selection of LSPs Setting general parameters We set up some basic parameters such as center frequency sample density and the position of the transmitter close all clear all set 0 defaultTextFontSize 14 4 Set default font size for the plots set 0 defaultAxesFontSize 14 s simulation_parameters 4 Basic simulation parameters s center_frequency 2 185e9 X Center Frequency s sample_density 4 h 4 samples half wave length 1 layout s 4 Create Layout Setting up a user track QuaDRiGa needs the positions of transmitter and receiver e g for calculating the polarization or the arrival and departure angels The positioning information of the Tx and Rx is essential also when the LSPs are not calculated The following generates a linear track with 20 m length having a direction The track is further split into 4 segments of 5 m length The splitting is done by calling the method split_segment of the track object The the first two arguments of that function are the minimum length of a segment 1 m and the maximum length of the segment 6 m Each existing segment that is longer then the maximum length is split into subsegments The length of those segments is random where the third and fourth parameter determine the mean and the STD of the length of new subsegment Hence t split seg
146. o name from track scenario e The transmitter name from layout tx_name e The receiver name from layout rx_name e The segment number After channel merge has been called the name string consists of e The transmitter name from layout txname e The receiver name from layout rx_name version Version number of the QuaDRiGa release that was used to create the channel object individual_delays Indicates if the path delays are identical on each MIMO link 0 or if each link has a different path delay 1 coeff The complex valued channel coefficients for each path The indices of the 4 D tensor are Rx Antenna Tx Antenna Path Snapshot delay The delays for each path There are two different options If the delays are identical on the MIMO links i e individual_delays 0 then delay is a 2 D matrix with dimensions Path Snapshot If the delays are different on the MIMO links then delay is a 4 D tensor with dimensions Rx Antenna Tx Antenna Path Snapshot initial_position The snapshot number for which the initial LSPs have been generated Normally this is the first snapshot However if the user trajectory consists of more than one seg ment then initial_position points to the snapshot number where the current segment starts For example If initial_position is 100 then snapshots 1 99 are overlapping with the previous segment tx_posit
147. of 1 ms which gives us 1000 samples per second Fig 33 left illustrates the results t movement_profile 0 7 5 0 6 0 20 20 dist t interpolate_movement 1e 3 ci cn interpolate dist t get_length nsnap ci no_snap time O nsnap 1 t movement_profile 1 end nsnap 1 figure plot time dist r xlabel Time s ylabel Position on track m The last plot Fig 33 right shows the PDP of the interpolated channel with the movement profile applied The channel starts in the second segment with a lot of fading goes back to the first while slowing down at the same time After staying constant for one second the channel starts running again speeding up towards the end of the track h ci fr 100 e6 512 h squeeze h pdp 10 logi0 abs ifft h 1 72 figure imagesc pdp 1 256 caxis max max pdp 50 max max pdp 5 J colorbar cm colormap hot colormap cm end 1 1 set gca XTick 1 32 255 set gca XTickLabel 0 32 256 100e6 1e6 xlabel Delay mus set gca YTick 1 ci no_snap 8 ci no_snap set gca YTickLabel 0 ci no_snap 8 ci no_snap ci no_snap 20 ylabel Time s Copyright Fraunhofer Heinrich Hertz Institute 101 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS close all disp QuaDRiGa Version simulation_parameters
148. om the close proximity of the scatterer to the initial Rx position The position of all 20 reflection points are calculated by the channel model Those position mark the position of the last bounce scatterer LBS When moving the Rx the distance to the LBS changes for each subpath and so does the phase Here the phase of each of the subpaths is calculated from the length of the path pow abs squeeze sum c coeff 1 1 2 plot distance 10 logi0 pow r xlabel Distance from track start point ylabel Tap power dB gt 5 72 This plot shows the power of the first NLOS tap along the track The fading is significantly higher in the beginning and becomes much less strong towards the end phase unwrap angle squeeze d coeff 1 1 2 plot distance phase xlabel Distance from track start point ylabel Continuous phase 200 7 r j 7 7 5 10fF 0 15 2 g 200p A of S fal 3 25 3 5 400 z 30 S 5 a E 35f 600F 6 40f 45 800 50f 1000 fi fi fi fi 55 Ll fi 1 20 25 30 35 40 45 50 20 25 30 35 40 45 50 Distance from track start point Distance from track start point Figure 26 Drifting phases and Tx power vs Rx position drifting phases tutorial Without drifting the phases of the subpaths are approximated by assuming that the angles to the LBSs do not change However th
149. omputations calculating the drifting polarization is also the most computing intense task To reduce this complexity the polarization is only updated when the arrival angle changed more than 0 2 degree since the last update Transmit Kpa Receiver Projection Plane Polarization Orientation Horizontal Pol Ey P Vector a rs i j Wa ae Arrival Angles Figure 13 Illustration of the angles and vectors used for the computation of the geometric LOS polarization Left Scheme of the projection Right Angles on the projection plane Model for the LOS component The principle of the model is depicted in Fig 13 The wave travel direction r is determined by the AoA at the receiver given in azimuth and elevation 0 direction The transmit polarization vector F results from the beam pattern 7 and lies in the plane perpendicular to r Note that our method is only valid for linearly polarized waves Circular polarization can be obtained by combining two linear elements with a phase offset The receive antenna can have any orientation in 3D space Thus we need additional information on the element orientation which is realized by a vector Orp Or represents the linear receiver polarization However this vector does not lie in the same plane as F does The polarization vector p results from a projection of the orientation vector o on the plane perpendicular to the travel direction Due to the orientation mismatch between transmit
150. on 2 2 2 rx_track Handles of track objects for each Rx See Section 2 2 3 scenario Name of the scenario text string scenpar The parameter table See Section 2 4 plpar Parameters for the path loss See Section 2 4 no_positions positions tx_position Number of receiver positions associated to this parameter_set object Note that each segment in longer tracks is considered a new Rx position The list of initial positions for which LSPs are generated This variable is obtained from the properties track initial_position and layout rx_position The transmitter position obtained from the corresponding layout tx_position ds kf sf asD asA esD esA xpr The RMS delay spread in s for each receiver position The Ricean K Factor linear scale for each receiver position The shadow fading linear scale for each receiver position The azimuth spread of departure in deg for each receiver position The azimuth spread of arrival in deg for each receiver position The elevation spread of departure in deg for each receiver position The elevation spread of arrival in deg for each receiver position The cross polarization ratio linear scale for each receiver position parameter_maps map_extension map _extent map size samples_per_meter map _valid LSP_xcorr_matrix LSP_matrix_isOK map_x_coord map y coord The seven large scale parameter maps in logarithmic scale Rows correspond to the y coordina
151. or 14 transmitter 63 66 68 71 unified modeling language 19 WINNER Wireless World Initiative for New Radio 10 11 23 42 48 54 59 66 70 72 WSS wide sense stationary 13 WSSUS wide sense stationary uncorrelated scattering 12 XPR cross polarization ratio 23 42 51 70 Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 List of Acronyms List of Symbols y Polarization rotation angle derived from the NLOS XPD dm Offset angle of the m th subpath relative to 0 K Cross polarization power ratio Carrier wavelength a Departure or Arrival vector in Cartesian Coordinates B Autocorrelation map of a large scale parameter er Position of the rt element in the receive array relative to the array center F 0 Antenna field pattern G Coefficient matrix of a tap between all ni Tx antennas and n Rx antennas Hy s MIMO channel matrix for all ny Tx antennas and n Rx antennas in frequency domain M Polarization coupling matrix 0 t Antenna orientation vector for the receiver transmitter Pr t Projection of the orientation vector 0 on the plane perpendicular to r r Wave travel direction in Cartesian coordinates Azimuth angle for arrival AoA for departure AoD LOS LOS direction seen from the receiver Wim s Phase of the m subpath of the lt cluster at snapshot position s p Correlation coefficient Tg Angular Spread Or RMS delay spre
152. or of the method usage 0 Deletes all existing parameters from the track usage 1 Deletes all existing parameters from the track and generates new ones Existing LSPs will be overwritten usage 2 default Keeps existing parameters but generates missing ones This is useful when for example the effective PG is provided but the other LSPs are unknown In this case the unknown gaps are filled with values which are generated from the provided scenario description check_parfiles check_parfiles 0 1 default 1 Disables 0 or enables 1 the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves execution time Output par The automatically generated parameters This cell array contains a parameter structure of the LSPs for each receiver with the following fields ds The delay spread in s per segment kf The Ricean K Factor in dB per snapshot pg The effective path gain in dB excluding antenna gains per snapshot asD The azimuth angle spread in deg per segment at the transmitter asA The azimuth angle spread in deg per segment at the receiver esD The elevation angle spread in deg per segment at the transmitter esA The elevation angle spread in deg per segment at the receiver xpr The NLOS cross polarization in dB per segment An identical copy of this variable is assigned to tr
153. orials 74 A 1 Network Setup and Parameter Generation osoo a ea e a 74 A2 pmulating a Measured Scenarios o e toso oe hd fag Be e bhe E ee ee aa De a 78 AS Generation of patellite Channels o ce eu cee ee eg RE ee ee oa e 82 A A Drifting Phases and Delays aoe ci 6044 ee EE ee ee ee ee es 90 A 5 Time Evolution and Scenario Transitions 0 0 00 eee ee 94 A 6 Applying Varying Speeds Channel Interpolation 0 00000008 99 AT Geometrie Polarization so 4 444446 2 82844 eb 44 SA eee ee a ee eS 103 AS Visuaizmge RHCP LHCP Patterns oo sho asa ce Pag dadceve aw eS oe Soe os 107 A 9 How to manually set LSPs in QuaDRiGa sssaaa csere 0 00 00 0 eee eee 109 Copyright Fraunhofer Heinrich Hertz Institute 3 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 List of Figures List of Figures 1 Simplified overview of the modeling approach used in QuaDRiGa 12 2 Typical driving GS so e aa e i e i a eA a ie eee ae Die ede e a ee Pe 15 3 UML class diagram of the model software ooo a a e 20 4 aDRiGa Data Flow so c mac teg 6a 4 Ee Se eek a ee a E a a a E a 47 5 Essential steps for the calculation of time evolving channel coefficients 54 6 WINNER system level approach showing several segments drops Source KMH 07 55 i Example patterns for a dipole antenna a aoao a 56 8 Principle of the generation of correlated LSPs aoaaa e 59 9 Principle of the map generation o o soosoo c
154. out a change in the environment type higher density of buildings but still the environment remains urban This is not explicitly modeled However the Satellite NLOS Urban map covers a typical range of parameters E g in a light NLOS area the received power can be some dB higher compared to an area with denser buildings The placement of light dense areas on the map is random Thus different characteristics of the same scenario are modeled implicit They are covered by the model but the user has no influence on where specific characteristics occur on the map when using the automatic mode An alternative would be to manually overwrite the automatically generated parameters or use the manual mode In order to update the LSPs and use a new set of parameters a new segment needs to be created Le here an environment change from Satellite NLOS Urban to the same Satellite NLOS Urban has to be created Thus a new set of LSPs is read from the map and new clusters are generated accordingly 9 Stopping at traffic lights NLOS This is the same as in point 5 10 Structural change in environment Houses have the same characteristics as before but are further away from the street urban environment with different reception characteristics Same as point 8 Copyright Fraunhofer Heinrich Hertz Institute 17 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW 11 Change of environment Urban
155. peed profile merges with lt lt input gt gt lt lt input gt gt array track name t initial_position no_snapshots positions movement_profile no_segments segment_index A Implements the correlation method for LSPs ground_direction height_direction closed compute directions copy_objects correct_overlap generate generate parameters get_length get_subtrack interpolate_ movement interpolate positions set_scenario set_speed split_segment visualize parameter_maps map_extension map_extent map_size samples_per_meter 5 map_valid LSP_xcorr_matrix LSP_matrix_isOK map_x_coord map_y_coord 1 1 creates 1 1 get_pl get_sf_profile supported scenarios static set_par t update_parameters lt lt control gt gt channel_builder get_channels get_los_channels Figure 3 UML class diagram of the model software Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 20 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 Description of Classes Properties and Methods In the following all properties and methods of the QuaDRiGa classes are described For the methods input and output variables are defined and explained There are three types of methods Standard methods require an instance of a class They are printed in black without the class name par generate paramete
156. pole lhcp dipole lhcp rhep dipole An isotropic radiator with vertical polarization A short dipole radiating with vertical polarization A half wave dipole radiating with vertical polarization A vertically polarized ideal patch antenna with 90 opening in azimuth and eleva tion An antenna with a custom gain in elevation and azimuth E g a generate custom 1 90 90 0 1 creates an array with 90 opening in az imuth and elevation and 0 1 rear gain Two elements with ideal isotropic patterns vertical polarization The second element is tilted by 90 Two crossed dipoles with one port The signal on the second element horizontal is shifted by 90 out of phase The two elements thus create a right hand circular polarized RHCP signal Two crossed dipoles with one port The signal on the second element horizontal is shifted by 90 out of phase The two elements thus create a left hand circular polarized LHCP signal Two crossed dipoles For input port 1 the signal on the second element is shifted by 90 out of phase For input port 2 the the signal on the second element is shifted by 90 out of phase Port 1 thus transmits a LHCP signal and port 2 transmits a RHCP signal ula2 Unified linear arrays composed of 2 omni antennas vertical polarization with 10 cm element distance ula4 Unified linear arrays composed of 4 omni antennas vertical polarization with 10 cm element distance ula8
157. r than the per cluster angular spread However even with many clusters the Doppler spread is narrower in QuaDRiGa than when assuming pure Rayleigh fading This is also in line Copyright Fraunhofer Heinrich Hertz Institute 12 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW with measurement results It can be observed in the field that the main components arrive from selected angles and the classical Doppler spectrum s Jakes or Butterworth filter shaped characteristics are only valid as long term average and not valid for a short time interval To summarize e A typical propagation environment requires 8 20 clusters e Internally each cluster is represented by 20 sub paths resulting in 160 400 sub paths in total e Each sub path is modeled as a single reflection e The 160 400 sub paths are weighted by the antenna response The 20 sub paths for each cluster are summed up which results in 8 20 paths e For a MIMO system with multiple antennas at the transmitter and receiver each path has as many channel coefficients as there are antenna pairs Hence at the output there are npagin NRe NT Channel coefficients 1 4 Continuous time evolution QuaDRiGa calculates the channel for each defined reception point To generate a time series a continuous track of reception points can be defined The arrival angles of the sub paths play a crucial for the time evolution because the phase ch
158. rack m ylabel Delay Spread ns legend LOS sigma_ tau 1 title Position dependant delay spread close all disp QuaDRiGa Version simulation_parameters version QuaDRiGa Version 1 0 1 145 Copyright Fraunhofer Heinrich Hertz Institute 88 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS Received power along the track Received Power per MIMO LINK dB 300 400 Track m Position dependant delay spread Delay Spread ns 0 100 200 300 400 500 600 Track m Figure 23 Results for the satellite channel tutorial Copyright Fraunhofer Heinrich Hertz Institute 89 eMail quadriga hbhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS A 4 Drifting Phases and Delays Drifting is an essential feature of the channel model Drifting enables a continuous time evolution of the path delays the path phases the departure and arrival angles and the LSPs It is thus the enabling feature for time continuous channel simulations Although drifting was already available in the SCME branch of the WINNER channel model it did not make it into the main branch Thus drifting is not available in the WIM1 WIM2 or WIM model Here the functionality is implemented again This script focuses on the delay and the phase component of the drifting functionality Channel model setup and coefficient
159. rack class does not handle transmitter positions a default position of 0 0 25 is assumed Please refer to layout generate_parameters for a more detailed description Input overlap The length of the overlapping part relative to the segment length When there are scenario transitions KF and PG change smoothly during a prede fined interval The length of that interval is a percentage of previous segment The parameter overlap adjusts this percentage ranging from 0 i e very hard step like change at the scenario boundary to 1 very smooth but long transition usage Changes the behavior of the method usage 0 Deletes all existing parameters from the track usage 1 Deletes all existing parameters from the track and generates new ones Existing LSPs will be overwritten usage 2 default Keeps existing parameters but generates missing ones This is useful when for example the effective path gain PG is provided but the other LSPs are unknown In this case the unknown gaps are filled with values which are generated from the provided scenario description check_parfiles check_parfiles 0 1 default 1 Disables 0 or enables 1 the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves execution time verbose Enables 1 default or disables 0 the progress bar Output par The automatically generated
160. rpolation to to obtain variable MT speeds step H target DS for each sub interval For example if the old segment yields a DS of 200 ns and the new segment has 400 ns then the target DS will be 220 ns for the first sub interval 240 ns for the second and so on Then we look for a combination of paths to ramp up down in each sub interval that best matches the DS along the overlapping area to the target area in a mean square sense 3 2 8 Postprocessing Variable Speeds Realistic channel traces incorporate arbitrary speeds accelerations and decelerations Provided that the channel sampling theorem is fulfilled we can interpolate the coefficients as it is illustrated in Fig 15 bottom The white dots represent the snapshots at a constant distance However the sample points gray stars can have an unequal spacing e g for an accelerated movement Each sample point in the time domain given in units of seconds has a corresponding sample point on the track in units of meters The amplitudes and phases of the channel coefficients are interpolated separately using a cubic spline interpolation The path delays are interpolated with a piecewise cubic hermite interpolating polynomial Copyright Fraunhofer Heinrich Hertz Institute 73 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 A TUTORIALS A Tutorials In the following we provide a variety of tutorials that can get you started with QuaDRiGa You can also use the MATLAB Help to a
161. rs overlap usage check_parfiles verbose Static methods can be called directly from the command line without creating an instance of the class first They are printed in blue h_array mse mse_pat array import_pattern fVi fHi correct_phase accuracy max_num_elements azimuth_grid elevation_grid verbose The constructor is a special method that is called when the class name is used as a function e g when calling a array dipole There is only one constructor for each class They are printed in blue h_array array array_type phi_3dB theta_3dB rear_gain Index of Methods e Class simulation_parameters simulation_parameters constructor set_speed e Class array array constructor apply_common_phase copy_element copy_objects estimate_common_phase estimate_pol_vector generate import_pattern static interpolate rotate_pattern set_grid visualize e Class track track constructor compute_directions copy_objects correct_overlap generate generate_parameters get_length get_subtrack interpolate movement interpolate_positions set_scenario set_speed split segment visualize e Class layout layout constructor create_parameter_sets Copyright Fraunhofer Heinrich Hertz Institute 21 eMail q
162. rtz Institute 35 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE par h_parset generate_parameters overlap usage check_parfiles Description Generates large scale parameters and stores them in track par Normally parameters are handled by objects of the parameter_set class which are gener ated by calling layout create_parameter_sets Those objects then feed the parameters to the channel_ builder However this method is rather inflexible when the user wants to manipulate the parameters directly As an alternative parameters can be provided in the property track par of the track class This allows the user to edit parameters without dealing with the parameter_set objects This function extracts the LSPs for the given scenario from the parameter_set class and stores them in track par Hence it automatically generates the LSPs and thus implements an easy to use interface for the parameter _set class Input overlap The length of the overlapping part relative to the segment length When there are scenario transitions KF and PG change smoothly during a prede fined interval The length of that interval is a percentage of previous segment The parameter overlap adjusts this percentage ranging from 0 i e very hard step like change at the scenario boundary to 1 very smooth but long transition usage Changes the behavi
163. rtz Institute 39 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE copy_objects Description A modified version of the standard physical copy function While the standard copy command creates new physical objects for each element of obj in case obj is an array of object handles copy_objects checks whether there are object handles pointing to the same object and keeps this information angles get_angles Description Calculates the departure and arrival angles of the LOS path between Tx and Rx Output angles A matrix containing the four angles Azimuth of Departure at the Tx AoD row 1 Azimuth of Arrival at the Rx AoA row 2 Elevation of Departure at the Tx EoD row 3 Elevation of Arrival at the Rx EoA row 4 The number of columns corresponds to the number of rx positions h_channel h_cb get_channels Description Calculate the channel coefficients Output h_channel A vector channel objects See Section 2 2 7 h_cb A vector of channel_builder objects See Section 2 2 6 dist get_distances Description Calculates the distances between Rx and Tx Output dist A vector containing the distances between each Rx and the Tx in m pl scale_sf ge t_pl evaltrack i mobile Description Calculates the path loss The path loss model is specified in the configura
164. s can be found Section 3 1 The user of the model needs to configure the network layout This includes Setting the transmitter position e g the BS positions or the satellite orbital position Defining antenna properties for the transmitter and the receiver Defining the user trajectory Defining states or segments along the user trajectory Assigning a propagation environment to each state Defining the user trajectory states along the user trajectory and related parameters is performed by the state sequence generator SSG In the current implementation different SSGs are available e Manual definition of all parameters by the user e g definition of short tracks e Statistical model for the journey A simple model mainly designed for demonstration and testing purpose is included in the tutorial satellite_channel e Derive trajectory and state sequence from the measurement data 2 Configuration files define the statistical properties of the LSPs For each state also called scenario a set of properties is provided Typically two configurations files are used e One for the good state also called LOS scenario e The other for the bad state NLOS scenario For each state QuaDRiGa generates correlated maps for each LSP For example the delay spread in the file is defined as log normal distributed with a range from 40 to 400 ns QuaDRiGa translates this distribution in to a series of discrete values
165. s describe the statistical properties of the XPR environment For the XPR no correlation map is calculated and the XPR is updated once per segment Note For the LOS component no XPR environment is assumed only the XPR antenna is applied Hence the overall XPR depends also highly on the K factor DS_mu log10 s Statistical properties of the delay spread DS _sigma log10 s DS_lambda meter AS_A_mu log10 deg Statistical properties of the azimuth of arrival spread at the receiver AS_A_sigma log10 deg AS_A_lambda meter ES_A_mu log10 deg Statistical properties of the elevation of arrival spread at the receiver ES_A sigma log10 deg ES_A_lambda meter AS_D_mu log10 deg Statistical properties of the azimuth of departure spread at the transmitter AS_D sigma log10 deg AS_D_lambda meter ES_D_mu log10 deg Statistical properties of the elevation of departure spread at the transmitter ES_D _sigma log10 deg ES_D_lambda meter Cross correlations There are interdependencies between parameters For example if the K Factor is high the delay spread gets shorter since more power is coming from the direct component This is expressed by the cross correlations parameters They can vary between 1 and 1 Negative values denote inverse correlation e g a high K Factor implies a low delay spread Positive Value implies a positive correlation such as a high K Factor also implies a high shadow fading Cross Correlations are used during t
166. s the azimuth sampling angles in radians ranging from 7z to m element _position Position of the antenna elements in local cartesian coordinates using units of m field_pattern_ vertical Vertical or theta component of the electric field given in spherical coordinates This variable is a tensor with dimensions elevation azimuth element describing the vertical or theta component of the far field of each antenna element in the array field_pattern_ horizontal Horizontal or phi component of the electric field given in spherical coordinates This variable is a tensor with dimensions elevation azimuth element describing the horizontal or phi component of the far field of each antenna element in the array common_phase A phase offset which is the same on both polarizations Internally QuaDRiGa can only use real valued patterns Complex valued patterns have to be split into at least two real valued patterns However if the phase is the same on both polarization components i e the antenna is linearly polarized but has a direction dependant phase then the phase might be stored here This variable is a tensor with dimensions elevation azimuth element By default it is initialized with zeros no phase offset pol_vector The orientation of the electric field in 3D local cartesian coordinates coupling Coupling matrix between elements This matrix describes a pre or postprocessing of
167. segment mi ma mu sig no_check Description Splits long segments in subsegments of the same type Input mi Minimum length of the subsegment in m default 10m ma Maximum length of the subsegment in m must be gt 2 mi default 30m mu Mean length of the subsegment mi lt mu lt ma default 15m sig Std of the length of the subsegment default 5m no_check Disable parsing of input variables default false visualize Description Plots the track Copyright Fraunhofer Heinrich Hertz Institute 34 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 4 Class layout Objects of this class define the network layout of a simulation run Each network layout has one or more transmitters and one or more receivers Each transmitter and each receiver need to be equipped with an antenna array which is defined by the array class In general we assume that the transmitter is at a fixed position and the receiver is mobile Thus each receivers movement is described by a track Properties name Name of the layout simpar Handle of a simulation_parameters object See Section 2 2 1 no_tx Number of transmitters or base stations no rx Number of receivers or mobile terminals tx_name Identifier of each Tx must be unique tx_position Position of each Tx in global cartesian coordinates using units of m tx_array Handles of array objects for each T
168. sely populated urban areas The max cell radius is about 1 km WINNER_UMi_B1_LOS WINNER_UMi B1_NLOS WINNER Urban Microcell For typical terrestrial pico base stations deployed below rooftop in densely populated urban areas The max cell radius is about 200 m WINNER _SMa_C1_LOS WINNER_SMa_C1_NLOS WINNER Sub Urban Macrocell For typical terrestrial base stations deployed above rooftop in sub urban areas The max cell radius is about 10 km WINNER _Indoor_A1_LOS WINNER _Indoor_Al_NLOS WINNER Indoor Hotspot For typical indoor deployments such as WiFi or femto cells WINNER_UMa2Indoor_C4_LOS WINNER_UMa2Indoor_C4_NLOS WINNER Urban Macrocell to Indoor For users within buildings that are connected to a terrestrial base station deployed above rooftop in densely populated urban areas WINNER_UMi2Indoor_B4_LOS WINNER_UMi2Indoor B4_NLOS WINNER Urban Microcell to Indoor For users within buildings that are connected to terrestrial pico base stations deployed below rooftop in densely populated urban areas BERLIN UMa_LOS BERLIN_UMa_NLOS Terrestrial Urban Macrocell parameters extracted from measurements in Berlin Ger many MIMOSA 10 45_LOS MIMOSA_10 45_NLOS MIMOSA Satellite to Mobile Parameters for Urban Propagation Elevation range from 10 to 45 Parameters were extracted from terrestrial measurement using a high attitude platform MIMOSA_16 25_LOS MIMOSA_16 25_NLOS MIMO
169. sential component of the polarization model is the orientation vector o which is not unique for circular polarized patterns A solutions is to virtually assemble a circular polarized antenna out of two linear elements These elements need to be crossed in a way that the transmission axes of both elements are perpendicular to each other Both elements are fed with the same signal but one of them is shifted by 90 out of phase This is modeled by using the channel coefficients 6 for each of the two elements and assembling them in a channel matrix G In order to get circular polarized coefficient matrix G we use the Jones vectors 20 as coupling matrices G GRR JRL CEGC 21 JLR JLL ACDA 2 1 4 guv 9HH i i Copyright Fraunhofer Heinrich Hertz Institute 58 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION 3 1 4 Generation of Correlated Large Scale Parameters QuaDRiGa models the auto and cross correlation properties of the LSPs by creating 2D maps for each of the following parameters RMS Delay Spread DS Ricean K Factor KF Shadow Fading SF Azimuth spread of Departure AsD Azimuth spread of Arrival AsA Elevation spread of Departure EsD Elevation spread of Arrival EsA POO ee S The map based method is already part of the WINNER implementation KMH t07 HMK 10 where the maps are generated by filtering random normal distributed numbers along the x and y axis of the map
170. seven for LOS and another seven for NLOS The parameters for calculating the channel coefficients are drawn from the second seven maps We get a set of channel coefficients with different properties e g more multipath components lower K Factor etc A smooth transition between the coefficients from the first segment and the second is realized by the ramping down the powers of the clusters of the old segment and ramping up the power of the new This is implemented in step 4 Post processing Copyright Fraunhofer Heinrich Hertz Institute 16 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 1 INTRODUCTION AND OVERVIEW 3 NLOS LOS Change This is essentially the same as in point 2 However since the third segment is also in the scenario Satellite LOS Urban no new maps are generated The parameters are extracted from the same map as for the starting segment 4 Turning off without change in reception condition LOS QuaDRiGa supports free 3D trajectories for the receiver Thus no new segment is needed the terminal stays in the same segment as in point 3 However we assume that the receive antenna is fixed to the terminal Thus if the car turns around so does the antenna Hence the arrival angles of all clusters including the direct path change This is modeled by a time continuous update of the angles delays and phases of each multipath component also known as drifting Due the change of the arrival angles and t
171. shaded area and back The second track also starts in the LOS area Here the scenario changes to another LOS segment and then to an NLOS segment The LOS LOS change will create new small scale fading parameters but the LSPs will be highly correlated between those two segments 1 layout s 4 New layout 1l no_rx 2 4 Two receivers 1 tx_array generate dipole 4 Dipola antennas at all Ra and Tz l rx_array 1 tx_array 1 tx_position 3 25 4 Elevate Tx to 25 m UMal BERLIN_UMa_LOS UMan BERLIN_UMa_NLOS l track 1 generate circular 20 pi 0 4 Circular track with 10m radius 1l track 1 initial_position 10 0 1 5 4 Start east running nord 1 track 1 segment_index 1 40 90 h Segments 1 track 1 scenario UMal UMan UMal l track 2 generate linear 20 0 4 Linear track 20 m length 1 track 2 initial_position 10 0 1 5 Same start point 1 track 2 interpolate_positions 128 20 l track 2 segment_index 1 40 90 1 track 2 scenario UMal UMal UMan 1l visualize 4 Plot all tracks l track interpolate_positions s samples_per_meter 1 track compute_directions Now we create the channel coefficients Fixing the random seed guarantees repeatable results i e the taps will be at the same positions for both runs Note that the computing time is significantly longer when drifting is enabled Copyright Fraunhofer Heinrich Hertz Institute 94 eMail
172. ssigned an environment In the QuaDRiGa terminology this is called a scenario E g the first segment on the track is in the Satellite LOS Urban scenario The selection of the scenario is done during the first step set up tracks scenarios antennas and network layout QuaDRiGa itself does not supply functions to perform the setting up of tracks and scenarios automatically However external scripts can be used to perform this task An example can be found in section A 3 A RHCP LHCP signal is defined in the antenna setup After the model setup the automatic mode generates a set of LSPs for this segment I e the second step of the model calculates one value for each of the 7 LSPs using the map based method Thus a set of seven maps is created for the scenario Satellite LOS Urban Those maps cover the entire track Thus the same maps are used for all Satellite LOS Urban segments of the track The third step then calculates a time series of fading coefficients for the first segment that have the properties of the LSPs from the map E g if one calculates the RMS DS from the coefficients one gets the same value as generated by the map in step 2 2 LOS NLOS Change A scenario change is defined along the track E g the second segment along the track gets assigned the scenario Satellite NLOS Urban Now a second set of maps is generated for all Satellite NLOS Urban segments So in total we now have 14 maps
173. t without a change in the environment type higher density of buildings but still the environment remains urban 9 Stopping at traffic lights NLOS 10 Houses have the same characteristics as before but are further away from the street urban environment with different reception characteristics 11 Change of environment Urban Forest 12 Turning off without change of environment NLOS Each simulation run in QuaDRiGa is done in three and an optional fourth step Set up tracks scenarios antennas and network layout Generate correlated LSPs Calculate the channel coefficients optional Post processing mw Se Those steps also need to be done for the above scenario However different aspects of the track are handled in different parts of the model Additionally the QuaDRiGa model supports two operating modes for handling the LSPs 1 The first default mode generates the correlated LSPs automatically based on a scenario specific parameter set This is done in step 2 and involves so called parameter maps 2 The manual mode does not generate LSPs automatically Here the user has to supply a list of parameters to the model The step 2 thus to be implemented by the user Steps 1 3 and 4 are identical for both modes The following list describes the modeling of the observed effects along the track when using the automatic mode 1 1 Start Environment Urban LOS reception of satellite signal Each segment along the track gets a
174. tage of the existing findings of the XPR If the XPR is identical for both polarization directions such as in the WINNER parameter tables then we can follow the approach from Zhou et al ZRP 05 and calculate an additional NLOS rotation matrix M as M Moy Muh cos y siny 70 Mhy Mhh siny cosy Following the notations in OCGD08 we get Mwl Imah cosy 2 mol T man sing 8 ue y arccot VXPR 72 XPR However when the XPR is different for the vertical and horizontal component OCGD08 QOHDD10 then we get three parameters _ Mv 2 XPR 5 Mna _ Mv CPR Mor Mal XPR p Mhol In order to fulfill all three we can combine two rotations one for the vertical and one for the horizontal component with a scaling operation We convert XPR and XPR to rotation angles 7 and yp using 72 and calculate M to cos tan Yh COS W TELE My Coe OAT OM VOPR 73 sin Ww COS Ww EPR Elliptic polarization is obtained when there is a phase difference between the horizontal and the vertical component This is included by a scaling matrix M 4 i 74 0 expjk The antenna dependent parameters 6 5 and a are handled as in the LOS case using 68 which results in a rotation matrix M v The transformations are then combined to L V2 nag M MIs M 0 M M 75 The normalization with the Frobenius norm M ensures that M does not change the pow
175. te and columns to the x coordinate The order of the maps third dimension is DS KF SF asD asA esD esA All values are logarithmic Distance in m that is added to each direction when generating maps Extent of the mpas in x and y direction xmin xmax ymin ymax in m Number of map pixels in x and y direction n_x_samples n_y_samples Resolution of the decorrelation maps in samples m This value is obtained from simulation_parameters map_resolution Indicates if maps contain valid data The Cross correlation matrix for the LSPs Determines if the XCorr matrix is positive definite The x coordinates in m for each pixel of the maps The y coordinates in m for each pixel of the maps Methods h_parset parameter_set scenario positions check_parfiles Description Creates a new parameter_set object Input scenario The scenario name for which the parameters should be loaded A list of supported scenarios can be obtained by calling parameter_set supported_scenarios positions The list of initial positions for which LSPs should be generated check_parfiles check_parfiles 0 1 default 1 Disables 0 or enables 1 the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves execution time Output h_parset A parameter_set object Copyright Fraunhofer Heinrich He
176. ted see Section 3 1 4 This can be disabled by setting initialize 0 Input initialize Enables 1 default or disables 0 the generation of the parameter maps If you want to adjust the parameters first use initialize 0 then adjust the parameters in the parameter_set objects and call update_parameters manually check_parfiles Enables 1 default or disables 0 the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves some execution time Output h_parset A matrix of parameter_set objects Rows correspond to the scenarios columns correspond to the transmitters See Section 2 2 5 h_cb A vector of channel_builder objects See Section 2 2 6 h_layout layout generate regular no_sites isd h_array Description generates a new multicell layout using a regular grid of BS positions Each BS has three sectors Input no _sites the number of sites This can be be 1 7 or 19 resulting in 3 21 or 57 sectors respectively isd the inter site distance between the BSs in m h_array the antenna array h_array is for one sector only It will be rotated to match the sector orientations and copied to all sites The broadside direction of the provided antenna must be 0 facing east Output h_layout The generated layout Copyright Fraunhofer Heinrich He
177. ter and receiver there will be an offset between p and the vector obtained from the receiver beam pattern F This offset is compensated by introducing a virtual polarizer that turns the polarization state of the wave in a way that it matches the orientation of the receiver Since we do not want to change the amplitude of the wave this is handled by other parts of the model nor the type of polarization we need to compute a rotation matrix Thus we have to determine the rotation angle 7 This can be done by the following procedure 1 To simplify the computations we rotate the coordinate system such that the wave travel direction r lies in y direction i e r 0 1 0 Thus we need to rotate the orientation vector o by p 7 2 in This value can be changed in simulation_parameters drifting_update_threshold Copyright Fraunhofer Heinrich Hertz Institute 68 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION azimuth direction and q 0 in elevation direction cos p sinp 0 z cosq sinp cosq cosp sing Or 62 sing sinp sinq cosp cosq o I 2 We calculate the projection of the receiver orientation vector on the projection plane Since the projection plane lies now in the x z plane due to the rotation of the coordinate system we simply omit the y component of o switch the x and z component and normalize the resulting vector to unit length The switching is done to obtain th
178. this case we wrap it around the unit circle by a modulo operation gl PI m mod 2r 1 40 In case of elevation spreads the possible range of elevation angles goes from 7 2 to 7 2 In this case we have to correct values of f outside of this range o for el Kia lt 5 and all az angles 4 Ve T g for elevation g gt S 41 gl m for elevation gil lt a 2 The positions of the transmitter Tx and receiver Rx are deterministic and so are the angles of the LOS component We correct the values of the angles to incorporate this position 5 4 4 P git g 4 g208 42 Finally the cluster path is split into 20 sub paths to emulate intra cluster angular spreads dim P cp bm 43 m is the sub path index cg is the scenario dependent cluster wise RMS angular spread and db is the offset angle of the m sub path from Table 11 Furthermore each of the 20 angle pairs Ofm Of mn at the Tx gets coupled with a random angle pair 7 Of at the Rx see KMHT07 Copyright Fraunhofer Heinrich Hertz Institute 63 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Table 11 Offset Angle of the m Sub Path from KMH 07 Sub path Offset angle Sub path Offset angle m dm degrees m dm degrees 1 2 0 0447 11 12 0 6797 3 4 0 1413 13 14 0 8844 5 6 0 2492 15 16 1 1481 7 8 0 3715 17 18 1 5195 9 10 0 5129 19 20 2 1551 D
179. tion dependent power xlabel Track m ylabel Power dB axis 0 500 min power 5 max power 5 legend LOS P_ total 4 grid on The following plot Fig 20 top right shows the distribution PDF of the received power for both the LOS and NLOS segments bins 150 2 80 p_los hist power los bins cn no_snap 100 p_nlos hist power setdiff 1 cn no_snap los bins cn no_snap 100 figure bar bins p_los p_nlos axis 124 5 83 0 ceil max p_los p_nlos grid on colormap Cool title Empirical PDF of the ac LOS and NLOS power xlabel P_ total dB ylabel Probability legend LOS NLOS 1 The next plot shows the RMS delay spread along the path Again shaded ares are for the LOS segments pow_tap squeeze sum sum abs cn coeff 2 1 2 pow_sum sum pow_tap 1 mean_delay sum pow_tap cn delay 1 pow_sum ds sqrt sum pow_tap cn delay 2 1 pow_sum mean_delay 2 ar zeros 1 cn no_snap ar los 10 figure a area dist ar set a i FaceColor 0 7 0 9 0 7 set a LineStyle none hold on plot dist ds 1le6 hold off ma 1e6 max ds 0 1 max ds axis 0 500 O ma title Position dependant delay spread xlabel Track m ylabel Delay Spread dB legend LOS sigma_ tau 1 grid on Copyright Fr
180. tion files and in parameter_set plpar Input evaltrack A track object for which the PL should be calculated If evaltrack is not given then the path loss is calculated for each Rx position Otherwise the path loss is calculated for the positions provided in evaltrack The Rx index If it is not given the PL is evaluated for all Rx positions If evaltrack is given and if simulation_parameters drifting precision is set to 3 then this parameter is required to select the Rx antenna array default 1 i mobile Output The path loss in dB In some scenarios the SF might change with increasing distance between Tx and Rx Hence the shadow fading provided by the parameter map has to be changed accordingly The second output parameter scale_sf can be used for scaling the logarithmic SF value from the map pl scale_sf sf kf get_sf_p rofile evaltrack i mobile Description This function returns the shadow fading and the K factor along the given track This function is mainly used by the channel builder class to scale the output channel coefficients The profile is calculated by using the data in the correlation maps and interpolating it to the positions in the given track Increasing the resolution of the maps also increases the resolution of the profile Input evaltrack A track object for which the SF and KF should be interpolated i mobile If sim
181. tituting the distance d by the relative distance dp of two pixels a er 23 oe ee Gane 24 k is the running filter coefficient index We cut the exponential decay function a at maximum distance of Ad and normalize it with vd The map size is determined by the distribution of the users in the scenario plus the length of the filter function This is also illustrated in Fig 9 where the user terminals are placed inside the black square The extension space is needed to avoid filter artifacts at the edges of the map We initialize the map with random normal distributed numbers Then we apply the filter 23 in vertical running from top to bottom and in horizontal from left to right direction BU X with X N 0 1 25 L4 d dpx4 BY J ak Byra 26 k 0 L4 dy dpx4 BP ak Byo k 27 k 0 Copyright Fraunhofer Heinrich Hertz Institute 60 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION Next we apply the second filter 24 on the diagonals of the map First we go from the top left to the bottom right and then we go from the bottom left to the top right A dpx1 Bil 3 bp Bye 28 k 0 L4 dy dpx4 B S be Stas 29 k 0 After the autocorrelations are applied the extension space is removed and values of the remaining map are scaled to have the desired distribution 4 0 The same procedure is repeated for all seven LSPs However the decorrelation distance d as well as u c for
182. troduced by Baum et al in an extension of the SCM BHS05 However it was not incorporated into the WINNER models and no evaluation was reported Besides the parameters from steps B and C drifting requires the position of each antenna element at the Tx and Rx Additionally Rx element positions need to be provided for each snapshot separately depending on the orientation and position of the Rx The following calculations are then done element wise where the indices r t l m s denote the element index of the Rx antenna r and the Tx antenna t the cluster number l the sub path number m and the snapshot number within the current segment s respectively last bounce scatterer __ dS A OS te SS oe Sea initial Rx location Lp an mi aN x r r t s Tx locati eae a al Rx location at snapshot s Rx tt g Tx location Tx scatterer sea weed of Ks C _ am ee S Dm LOS e aw ere oF OS hot ae an i ro Lom positions dros p eee o 7d d Fits 7777 initial Rx location LOS s Figure 12 Illustration of the calculation of the scatterer positions and updates of the arrival angles for NLOS top and LOS bottom NLOS drifting For the NLOS paths we calculate the position of the last bounce scatterer LBS from the initial arrival angles and the cluster delays Then we update the angles and path lengths between the last bounce scatterer LBS and the terminal for each snapshot on the track This is done for each
183. tude The receiver latitude coordinate on the earth surface in deg Default is 52 5 sat_el Satellite elevation in deg Default is 31 6 sat_az Satellite azimuth in deg given in compass coordinates Default is 180 south sat_height Satellite height in km relative to earth surface Default is 35786 GEO orbit tx_no The tx_no in the layout object for which the position should be set Default is 1 Output pos The satellite positions in the metric QuaDRiGa coordinate system visualize Description Plots the layout Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 38 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 5 Class parameter_set This class implements all functions that are necessary to generate and manage correlated LSPs It also provides interfaces for the channel builder LSPs are the shadow fading the Ricean K Factor the RMS delay spread and the four angles elevation and azimuth at the transmitter and receiver This class implements some core functions of the channel model and the user does normally not need to interact with it However if parameter tables need to be changed here is the place to do so Properties name Name of the parameter_set object simpar Handle of a simulation_parameters object See Section 2 2 1 tx_array Handles of array objects for each Tx See Section 2 2 2 rx_array Handles of array objects for each Rx See Secti
184. tute 56 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 3 TECHNICAL DOCUMENTATION angles which include the effect of the orientation change Rotations in 3D are easier in Cartesian coordinates We therefore transform the original angle pair into a vector a that describes the arrival or departure angles in Cartesian coordinates The three vector elements represent the x y and z component cos cos 0 a 0 sing cos 8 sin 6 We now use a 3 X 3 matrix Q to describe the orientation change in 3D space In principal we can calculate QT for any arbitrary rotation axis and angle The example in Fig 7 was tilted by 20 around the z axis of the coordinate system The corresponding matrix is 1 0 0 Q 20 0 cos 20 sin 20 9 0 sin 20 cos 20 By multiplying Q with 8 we include the orientation change in the vector at 9 Q7 g al 0 10 Since a is now also in Cartesian coordinates and we need the transformed pattern F in spherical coordinates we have to transform at back to spherical coordinates This results in the new angles O 4 0 arctan ay 0 a3 o 4 11 O A arcsin a7 4 12 ay y and at are the x y and z component of a respectively We now get the coefficients of the rotated pattern by reading the original pattern F at the transformed angles Fy y 0 Fyn 0 13 Since the patterns are sampled at a fixed angular gri
185. uadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE e Class e Class e Class generate static generate_parameters get_channels power_map randomize_rx_positions set_pairing set_satellite_pos visualize parameter_set parameter_set constructor copy_objects get_angles get_channels get_distances get_pl get_sf_profile set_par supported _scenarios static update_parameters channel_builder channel_builder constructor get_channels get_los_channels static channel channel constructor fr interpolate merge Copyright Fraunhofer Heinrich Hertz Institute eMail quadriga hhi fraunhofer de 22 QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 1 Class simulation_parameters This class controls the simulation options and calculates constants for other classes Properties sample_density The number of samples per half wave length Sampling density describes the number of samples per half wave length To fulfill the sampling the orem the minimum sample density must be 2 For smaller values interpolation of the channel for variable speed is not possible On the other hand high values significantly increase the computing time significantly A good value is around 4 samples_per_meter Samples per meter This parameter is linked to the sample density by SD Ja 2 ae where fc is the carrier frequency in Hz SD is the sample density and
186. ulation_parameters drifting_precision is set to 3 then this parameter is required to select the Rx antenna array Output sf The shadow fading linear scale along the track kf The K factor linear scale along the track set_par name value Description Sets the parameters of all objects in parameter_set arrays This function sets all values of the parameter specified by name of the parameter_set array to the given value Example set_par ds 1e 9 sets all ds values to 1 ns Input name The fieldname that should be altered Supported are ds kf sf asD asA esD esA samples_per_meter map_extension and LSP_xcorr_matrix value The value that should be assigned If the LSP_xcorr_matrix is altered then the lower triangular part of the matrix is ignored and replaced by a transpose of the upper triangular matrix Copyright Fraunhofer Heinrich Hertz Institute 40 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE scenarios file names file_dir parameter_set supported_scenarios parse_shortnames Description Returns a list of supported scenarios Input parse_shortnames This optional parameter can disable 0 the shortname parsing This is significantly faster By default shortname parsing is enabled 1 Output scenarios A cell array containing th
187. used synonymously A cluster describes an area where many scattering events occur simultaneously e g at the foliage of trees or at a rough building wall In QuaDRiGa each scattering cluster is approximated by 20 individual scatterers Each one is modeled by a single reflection The 20 signals can be resolved in spatial domain where they have a typical angular spread of 1 6 However they cannot be resolved in delay domain Therefore in the output of the channel model these 20 signals also named sub paths are combined into a single signal which is represented by a path The difference to Rayleigh fading models which use wide sense stationary uncorrelated scattering WSSUS taps instead of paths is that each path has a very limited angular spread 1 6 which also results in a narrow Doppler spectrum The terms path multipath component MPC and tap are also used synonymously in the QuaDRiGa documentation To emulate a rich scatting environment with a wider angular spread many scattering clusters are created QuaDRiGa supports up to 42 clusters However depending on the angular spread and the amount of diffuse scatting which is approximated by discrete clusters in QuaDRiGa typical values are around 10 cluster for LOS propagation and 20 clusters for non LOS The positioning of the clusters is controlled by the environment angular spread and the delay spread The environment angular spread has values of around 20 90 and is typically much large
188. vation of arrival EoA 0 They share the same calculation method but have a different angular spread og We assume that the power angular spectrum of all clusters follows a wrapped Gaussian distribution KMH 07 PMF97 1 p P W 203 37 The wrapping is applied later by 40 when the discrete cluster angles are drawn from the statistics Since the above formula assumes a continuous spectrum and the channel model on the other hand uses discrete paths we need to correct the variance by a function Cg L K depending on the number of clusters L and the KF K This formula is derived in the Appendix It ensures that the input variance og is correctly reflected in the generated angles We obtain the angles by first normalizing the power angular spectrum so that its maximum has unit power We can thus omit the scaling factor 1 04 27 We also normalize the path powers P 34 so that the strongest peak has unit power which corresponds to an angle 0 All other paths get relative departure or arrival angles depending on their power 1 To 2 In P P 38 The value og is measured in radians here Next we create two random variables X and Y where X 1 1 is the positive or negative sign and Y M 0 o 10 introduces a random variation on the angle Then we calculate gt i gPl Xi o Y 39 If the power P of a path is small compared to the strongest peak its angle oP might exceed 7 In
189. w the threshold in dBm the link gets deactivated tx_power A vector of tx powers in dBm for each transmitter in the layout This power is applied to each transmit antenna in the tx antenna array By default if tx_power is not given 0 dBm are assumed check_parfiles Disables 0 or enables 1 default the parsing of shortnames and the validity check for the config files This is useful if you know that the parameters in the files are valid In this case this saves execution time Output pairs An index list of links for which channel are created The first row corresponds to the Tx and the second row to the Rx An identical copy gets assigned to layout pairing power A matrix containing the estimated receive powers for each link in dBm Rows cor respond to the receiving terminal columns correspond to the transmitter station For MIMO links the power of the strongest MIMO sublink is reported pos set_satellite_pos rx_latitude sat_el sat_az sat_height tx_no Description Calculates the Tx position from a satellite orbit QuaDRiGas reference coordinate system is on the surface of the earth In order to use QuaDRiGa for satellite links the satellite position must be set Normally this position is given in azimuth and elevation relative to the users position This function takes a satellite orbital position and calculates the corresponding transmitter coordinates Input rx_lati
190. wwe QuaDRiGa v1 2 3 307 A TUTORIALS Next we create a track object and pass the points along the track We then use the internal interpolation functions to interpolate the track to 1 point per meter t track 4 Create a track object t positions real point imag point zeros 1 numel point t interpolate_positions 1 4 Interpolate to 1 point per meter We now assemble a rudimentary state sequence generator that generates different states along the track We first define the distribution parameters of the segment length and then calculate the segments themselves The two possible states are MIMOSA_10 45_LOS which stands for LOS or good state and MIMOSA_10 45_NLOS for NLOS or bad state segment_length_mu 30 4 Average segment length in m segment_length_sigma 12 4 Standard deviation in m min_segment_length 10 4 Minimum segment length in m Now we define the segments the states along the track ind 1 while ind lt t no_snapshots 4h Each scenario has a 50 probability if rand lt 0 5 t scenario t no_segments MIMOSA_10 45_LOS else t scenario t no_segments end gt MIMOSA_10 45_NLOS 4 Get the length of the current segment segment_length randn segment_length_sigma segment_length_mu while segment_length lt min_segment_length segment_length randn segment_length_sigma segment_length_mu end segment_length round segment_
191. x See Section 2 2 2 rx_name Identifier of each Tx must be unique rx_position Initial position of each Rx relative to track start in global cartesian coordinates using units of m rx_array Handles of array objects for each Rx See Section 2 2 2 track Handles of track objects for each Rx See Section 2 2 3 pairing An index list of links for which channel are created The first row corresponds to the Tx and the second row to the Rx no_links Number of links for which channel coefficients are created read only Methods h_layout layout simpar Description Creates a new layout object Input simpar Handle of a simulation_parameters object See Section 2 2 1 h_parset h cb create_parameter_sets initialize check_parfiles Description Creates parameter_set objects based on layout specification This function processes the data in the layout object First all tracks in the layout are split into subtracks Each subtrack corresponds to one segment Then then scenario names are parsed A parameter_set object is created for each scenario and for each transmitter For example if there are two terrestrial BSs each having urban LOS and NLOS users then 4 parameter_set objects will be created BS1 LOS BS2 NLOS BS2 LOS and BS2 NLOS The segments are then assigned to the parameter_set objects In the last step the parameter maps are crea
192. y data for the maps and the LSPs Any existing maps will be deleted Data and maps will be declared as invalid and the next time when update_parameters is called new parameters are generated Values in layout track par will NOT be affected Copyright Fraunhofer Heinrich Hertz Institute 41 eMail quadriga hhi fraunhofer de QuaDRiGa v1 2 3 307 2 SOFTWARE STRUCTURE 2 2 6 Class channel_builder This class implements all functions that are needed to generate the channel coefficients It thus implements the core components of the channel model The class holds all the input variables as properties It s main function get_channels then generates the coefficients The procedure is summarized as follows The channel builder first generates a set of random clusters around each receiver This is done by drawing random variables for the delay the power and the departure and arrival angles for each cluster Each cluster thus represents the origin of a reflected and scattered signal The clusters are then represented as taps in the final CIR The random variables fit the distributions and correlations defined by the parameter_set object Next antenna dependent parameters are extracted for each user Those depend on the position of the terminal its orientation and the equipped antennas The polarization rotation of the NLOS taps is modeled by a random variable which fits to the distribution defined by the
193. ystems Proc IEEE PIMRC 05 1 512 516 2005 Ernst Eberlein Thomas Heyn Frank Burkhardt Stephan Jaeckel Lars Thiele Thomas Haustein Gerd Sommerkorn Martin K ske Christian Schneider Maria Dominguez and Joel Grotz Characterisation of the MIMO channel for mobile satellite systems acronym MI MOSA TN8 2 final report Technical Report v1 0 Fraunhofer Institute for Integrated Circuits IIS 2013 M Gudmundson Correlation model for shadow fading in mobile radio systems ET Electron Lett 27 23 2145 2146 November 1991 M Hata Empirical formula for propagation loss in land mobile radio services EEE Trans Veh Technol 29 3 317 325 1980 Petteri Heino Juha Meinila Pekka Ky sti et al CELTIC CP5 026 D5 3 WINNER final channel models Technical report 2010 S Jaeckel L Raschkowski K Borner L Thiele F Burkhardt and E Eberlein QuaDRiGa Quasi Deterministic Radio Channel Generator User Manual and Documentation Technical Report v1 1 0 248 Fraunhofer Heinrich Hertz Institute 2014 Pekka Ky sti Juha Meinila Lassi Hentil et al IST 4 027756 WINNER II D1 1 2 v 1 1 WINNER II channel models Technical report 2007 L Materum J Takada I Ida and Y Oishi Mobile station spatio temporal multipath clus tering of an estimated wideband MIMO double directional channel of a small urban 4 5 GHz macrocell EURASIP J Wireless Commun Netw 2009 M Narandzic M Kaske C Schneider M

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