Channel Models

NeoRadium currently supports two stochastic channel models: Clustered Delay Line (CDL) and Tapped Delay Line (TDL). These models are implemented as CdlChannel and TdlChannel classes based on 3GPP TR 38.901. Additionally, NeoRadium provides the TrjChannel class, which is based on UE trajectories created using ray-tracing scenarios. The deterministic aspects of the TrjChannel class are primarily derived from 3GPP TR 38.901 Section 8.4. Furthermore, you can derive your own customized channel models from the ChannelModel class, as explained below.

Channel Model Base Class

This module serves as the foundation for channel models. Currently, NeoRadium supports three types of channel models: CDL, TDL, and Trajectory-based channel models. Each model is derived from the ChannelModel class defined in this module. For more information about these subclasses, please refer to CdlChannel, TdlChannel, or TrjChannel.

class neoradium.channelmodel.ChannelModel(bwp, **kwargs)

This is the base channel model class that handles higher level processing, such as creating Channel Impulse Response (CIR) and channel matrices and applying the channel to a time-domain Waveform or a frequency-domain resource Grid.

Almost all interactions with channel models are done using the methods of this class. The derived classes mostly implement the lower level processes such as how the channel multipath information is obtained or how MIMO antenna arrays are processed in connection with the channel multipath properties.

Parameters:

  • bwp (BandwidthPart) – The bandwidth part object used by the channel model to create channel matrices.

  • kwargs (dict) –

    A set of optional arguments.

    normalizeGains:

    A boolean flag. The default value is True, indicating that the path gains are normalized before they are applied to the signals.

    normalizeOutput:

    A boolean flag. The default value is True, indicating that the gains are normalized based on the number of receive antennas.

    txDir:

    A string that represents the transmission direction, which can be either “Downlink” or “Uplink”. By default, it is set to “Downlink”.

    filterLen:

    The length of the channel filter. The default is 16 samples.

    delayQuantSize:

    The size of delay fraction quantization for the channel filter. The default is 64.

    stopBandAtten:

    The Stop-band attenuation value (in dB) used by the channel filter. The default is 80dB.

    seed:

    The seed used by the random functions in the channel model. Setting this to a fixed value ensures that the channel model generates repeatable results. The default value is None, indicating that this channel model uses the NeoRadium’s global random number generator.

    dopplerShift:

    The maximum Doppler shift in Hertz. The default value is 40 Hertz, which corresponds to a speed of approximately 10 kilometers per hour. A value of zero makes the channel model static. For trajectory-based channel models, this value is automatically assigned based on the maximum trajectory speed.

    carrierFreq:

    The carrier frequency of the channel model in hertz. The default is 3.5 GHz.

Other Properties:

All of the parameters mentioned above are directly available. Here is a list of additional properties:

coherenceTime:

The Coherence time of the channel model in seconds. This is calculated based on the dopplerShift parameter.

sampleRate:

The sample rate used by this channel model. For 3GPP standard, this is set to 30,720,000 samples per second.

print(indent=0, title=None, getStr=False)

Prints the properties of this channel model object.

Parameters:

  • indent (int) – The number of indentation characters.

  • title (str or None) – If specified, it is used as a title for the printed information. If None (default), the text “Channel Model Properties:” is used for the title.

  • getStr (Boolean) – If True, returns a text string instead of printing it.

Returns:

If the getStr parameter is True, then this function returns the information in a text string. Otherwise, nothing is returned.

Return type:

None or str

restart(restartRanGen=False, applyToBwp=True)

Resets the state of this channel model to the initial state.

Parameters:

  • restartRanGen (Boolean) – If a seed was not provided to this channel model, this parameter is ignored. Otherwise, if restartRanGen is set to True, the random number generator of this channel model is reset. If restartRanGen is False (the default), the random number generator is not reset. This means that if restartRanGen is False, for stochastic channel models, calling this function starts a new sequence of channel instances, which differs from the sequence when the channel was instantiated.

  • applyToBwp (Boolean) – If set to True (the default), this function restarts the BandwidthPart associated with this channel model. Otherwise, the BandwidthPart state remains unchanged.

goNext(applyToBwp=True)

This method is called after each application of the channel to a signal to move to the next slot. It advances the channel model’s internal variable that keeps track of the current time.

Parameters:

applyToBwp (Boolean) – If set to True (the default), this function advances the timing state of the BandwidthPart associated with this channel model. Otherwise, the BandwidthPart state remains unchanged.

getMaxDelay()

Calculates and returns the maximum delay of this channel model in time-domain samples for the current slot.

Returns:

The maximum delay of this channel model in number of time-domain samples for the current slot.

Return type:

int

applyToGrid(grid)

This function applies the channel model to the transmitted resource grid object specified by grid in the frequency domain. It then returns the received resource grid in a new Grid object. The function first calls the getChannelMatrix() method to obtain the channel matrix. Subsequently, it invokes the applyChannel() method of the Grid class to calculate the received resource grid.

Parameters:

grid (Grid) – The transmitted resource grid. An Nt x L x K Grid object, where Nt represents the number of transmit antennas, L denotes the number of OFDM symbols, and K is the number of subcarriers in the resource grid.

Returns:

A resource grid containing the received signal. An Nr x L x K Grid object, where Nr represents the number of receive antennas, L denotes the number of OFDM symbols, and K is the number of subcarriers in the resource grid.

Return type:

Grid

getChannelMatrix()

This method calculates and returns the channel matrix at the current time. The channel matrix is a 4-D complex NumPy array with dimensions L x K x Nr x Nt, where L represents the number of OFDM symbols, K denotes the number of subcarriers, Nr is the number of receive antennas, and Nt indicates the number of transmit antennas. Please refer to the notebook Channel Matrix for an example of using this function.

Returns:

A 4-D complex NumPy array with dimensions L x K x Nr x Nt, where L represents the number of OFDM symbols, K denotes the number of subcarriers, Nr is the number of receive antennas, and Nt indicates the number of transmit antennas.

Return type:

4-D complex numpy array

applyToSignal(inputSignal)

This method applies the channel model to the time-domain waveform specified by inputSignal and returns another Waveform object containing the received signal in time domain.

Parameters:

inputSignal (Waveform) – The transmitted time-domain waveform. An Nt x Ns Waveform object, where Nt represents the number of transmit antennas and Ns denotes the number of time samples in the transmitted waveform.

Returns:

A Waveform object containing the received signal. It is an Nr x Ns Waveform object where Nr denotes the number of receive antennas and Ns represents the number of time samples in the received waveform.

Return type:

Waveform

getChannelGains()

This function calculates the path gains for the current slot at the beginning of each symbol. The result is a 4-D tensor of shape L x Nr x Nt x Np, where L represents the number of symbols per slot, Nr and Nt indicate the number of receiver and transmitter antennas, respectively, and Np denotes the number of paths. The path gains are normalized based on the normalizeOutput and normalizeGains values.

Returns:

The path gains as a numpy array of shape L x Nr x Nt x Np.

Return type:

4-D complex numpy array

CDL Channel Model

This module implements the CdlChannel class which encapsulates the functionality of the Clustered Delay Line (CDL) channel model.

class neoradium.cdl.CdlChannel(bwp, profile='A', **kwargs)

This class implements the Clustered Delay Line (CDL) channel model based on 3GPP TR 38.901. It is derived from the ChannelModel class.

All of the API functions used in typical use cases are explained in the documentation of the ChannelModel class.

The typical use case involves instantiating a CdlChannel object and then calling functions such as getChannelMatrix(), applyToSignal(), applyToGrid(), etc. Please refer to the notebook Channel Matrix for an example of using this class.

Parameters:

  • bwp (BandwidthPart) – The bandwidth part object used by the channel model to create channel matrices.

  • profile (str or None) – The CDL profile. It can be one of ‘A’, ‘B’, ‘C’, ‘D’, ‘E’, or None. See 3GPP TR 38.90, Section 7.7.1 for more information. Use None to indicate a customized version of CDL channel (See Customizing CDL Model).

  • kwargs (dict) –

    Here’s a list of additional optional parameters that can be used to further customize this channel model:

    normalizeGains:

    A boolean flag. The default value is True, indicating that the path gains are normalized before they are applied to the signals.

    normalizeOutput:

    A boolean flag. The default value is True, indicating that the gains are normalized based on the number of receive antennas.

    txDir:

    A string that represents the transmission direction, which can be either “Downlink” or “Uplink”. By default, it is set to “Downlink”.

    filterLen:

    The length of the channel filter. The default is 16 samples.

    delayQuantSize:

    The size of delay fraction quantization for the channel filter. The default is 64.

    stopBandAtten:

    The stop-band attenuation (in dB) used by the channel filter. The default is 80 dB.

    seed:

    The seed used by the random functions in the channel model. Setting this to a fixed value ensures that the channel model generates repeatable results. The default value is None, indicating that this channel model uses the NeoRadium’s global random generator.

    dopplerShift:

    The maximum Doppler shift in Hertz. The default value is 40 Hertz, which corresponds to a speed of approximately 10 kilometers per hour. A value of zero makes the channel model static. For trajectory-based channel models, this value is automatically assigned based on the maximum trajectory speed.

    carrierFreq:

    The carrier frequency of the channel model in Hz. The default is 3.5 GHz.

    delaySpread:

    The delay spread in nanoseconds. The default is 30 ns. It can also be a string containing one of the values in following table (See 3GPP TR 38.901, table 7.7.3-1)

    Delay Spread str

    Delay spread

    ’VeryShort’

    10 ns

    ’Short’

    30 ns

    ’Nominal’

    100 ns

    ’Long’

    300 ns

    ’VeryLong’

    1000 ns
    ueDirAZ:

    This is a list of two angles for the Azimuth and Zenith of the UE’s direction of movement in degrees. The default value is [0, 90], which indicates movement along the x-axis. In the current version, the base station is assumed to be stationary.

    txAntenna:

    The transmitter antenna, which is an instance of AntennaElement, AntennaPanel, or AntennaArray class. If not specified, a single antenna element is automatically created by default.

    rxAntenna:

    The receiver antenna, which is an instance of AntennaElement, AntennaPanel, or AntennaArray class. If not specified, a single antenna element is automatically created by default.

    txOrientation:

    The orientation of the transmitter antenna. This is a list of three angle values in degrees: bearing angle (math:alpha), downtilt angle (math:beta), and slant angle (math:gamma). The default orientation is [0,0,0]. For more information, please refer to 3GPP TR 38.901, Section 7.1.3.

    rxOrientation:

    The orientation of receiver antenna. This is a list of three angle values in degrees: bearing angle (math:alpha), downtilt angle (math:beta), and slant angle (math:gamma). The default orientation is [180,0,0]. For more information, please refer to 3GPP TR 38.901, Section 7.1.3.

    kFactor:

    The K-Factor (in dB) used for scaling. The default is None. If not specified (kFactor=None), K-factor scaling is disabled.

    xPolPower:

    The cross-polarization Power in dB. The default is 10 dB. For more details please refer to “Step 3” in 3GPP TR 38.901, Section 7.7.1.

    angleScaling:

    The Angle Scaling parameters. If specified, it must be a tuple of 2 numpy arrays.

    The first item specifies the mean values for angle scaling. It’s a 1-D NumPy array containing four values for: the Azimuth angle of Departure, Azimuth angle of Arrival, Zenith angle of Departure, and Zenith angle of Arrival.

    The second item specifies the RMS angle spread values. It is a 1-D numpy array containing four RMS values for: the Azimuth angle of Departure, Azimuth angle of Arrival, Zenith angle of Departure, and Zenith angle of Arrival. For more information, please refer to Angle Scaling below.

    If this value is set to None (the default), the Angle Scaling is disabled.

    pathDelays:

    Use this parameter to customize or override the default path delays, which are set based on the CDL channel model as defined in 3GPP TR 38.901. In most use cases, you don’t need to specify this parameter. For more information, see Customizing CDL Model below.

    pathPowers:

    Use this parameter to customize or override the path power settings, which are set by default based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, refer to Customizing CDL Model below.

    aods:

    Use this parameter to customize or override the Azimuth angles of Departure, which are set by default based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    aoas:

    Use this parameter to customize or override the Azimuth angles of Arrival, which are set by default based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    zods:

    Use this parameter to customize or override the Zenith angles of Departure, which are set by default based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    zoas:

    Use this parameter to customize or override the Zenith angles of Arrival, which are set by default based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    angleSpreads:

    Use this parameter to customize or override the RMS Angle spread (in degrees) used to normalize angles. This parameter specifies four values corresponding to the Azimuth angle of Departure, Azimuth angle of Arrival, Zenith angle of Departure, and Zenith angle of Arrival. By default, these values are set based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    Please note that this should not be confused with angle spread values used for angle scaling (See angleScaling above).

    hasLos:

    Use this parameter to customize or override the hasLos property of this channel model. By default, this property is set based on the CDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. For more information, see Customizing CDL Model below.

    kFactorLos:

    Use this parameter when customizing the CDL model. It represents the K-Factor ratio (in dB) for the LOS (Line of Sight) cluster (the first cluster). You don’t need to specify this parameter for most use cases. For more information, refer to Customizing CDL Model below. By default, when customizing the CDL model, this value is set to the difference in path powers (in dB) between the first and second clusters.

Note

All angle values provided to this class are in degrees. However, internally, the class uses radian values for all calculations. Therefore, when you access any of the angle values, such as aods, aoas, zods, and zoas, remember that they are in radians.

Other Properties:

All of the parameters mentioned above are directly available. Here is a list of additional properties:

coherenceTime:

The Coherence time of the channel model in seconds. This is calculated based on the dopplerShift parameter.

sampleRate:

The sample rate used by this channel model. For 3GPP standard, this is set to 30,720,000 samples per second.

nrNt:

A tuple of the form (nr,nt), where nr and nt are the number of receiver and transmitter antennas elements correspondingly.

rayCoupling:

This property is used internally for ray coupling. See Step 2 in 3GPP TR 38.901, Section 7.7.1 for more details.

initialPhases:

The random initial phases used when creating channel gains. See Step 10 in 3GPP TR 38.901, Section 7.5 for more details.

Angle Scaling:

If angleScaling is set to None, angle scaling is disabled. Otherwise, it is applied to all angles of arrival and departure for all clusters, as per 3GPP TR 38.901, Section 7.7.5.1 and Annex A.

Customizing CDL Model:

There are two different ways to customize the CDL model:

  1. You can select one of the predefined CDL profiles (A, B, C, D, or E) and then modify the model’s parameters by providing additional information. For instance, you can choose the CDL-B model and override the standard path delays by specifying your own path delays.

  2. You can also create your own model entirely from scratch. Initially, pass None for the profile parameter and then specify all the channel model parameters. Please note that in this case, you must specify at least the following parameters:

    • pathDelays

    • pathPowers

    • aods

    • aoas

    • zods

    • zoas

    • hasLos

    You can also optionally specify the following values:

    • angleSpreads (defaults to [4.0, 10.0, 2.0, 2.0] if not specified)

    • kFactorLos (defaults to pathPowers[0]-pathPowers[1])

    Also note that if your channel model contains a LOS cluster, it must be the first cluster in the lists, and the hasLos parameter should be set to True.

print(indent=0, title=None, getStr=False)

Prints the properties of this CDL channel model object.

Parameters:

  • indent (int) – The number of indentation characters.

  • title (str or None) – If specified, it serves as the title for the printed information. If None (the default), an automatic title is generated based on the channel model parameters.

  • getStr (Boolean) – If True, returns a text string instead of printing it.

Returns:

If the getStr parameter is True, then this function returns the information in a text string. Otherwise, nothing is returned.

Return type:

None or str

restart(restartRanGen=False, applyToBwp=True)

This method first re-initializes the random object if a seed was provided to this channel model and the restartRanGen parameter is set to True. It then randomly re-initializes the ray coupling and initial phases and calls the base class restart().

Parameters:

  • restartRanGen (Boolean) – If a seed was not provided to this channel model, this parameter is ignored. Otherwise, if restartRanGen is set to True, this channel model’s random generator is reset and if restartRanGen is False (default), the random generator is not reset. This means if restartRanGen is False, calling this function starts a new sequence of channel instances, which differs from the sequence when the channel was instantiated.

  • applyToBwp (Boolean) – If set to True (the default), this function restarts the BandwidthPart associated with this channel model. Otherwise, the BandwidthPart state remains unchanged.

classmethod getChanGen(numChannels, bwp, profiles='ABCDE', delaySpread=(10, 500), ueSpeed=(10, 70), ueDir=(0, 360), **kwargs)

Returns a generator object that can generate CDL channel matrices based on the given parameters and criteria.

Refer to the notebook Creating random CDL channels for a dataset for an example of using this method.

Parameters:

  • numChannels (int) – The number of channel matrices generated by the returned generator.

  • bwp (BandwidthPart) – The bandwidth part object used by the returned generator to construct channel matrices.

  • profiles (str) – A string containing a combination of upper case letters ‘A’, ‘B’, ‘C’, ‘D’, and ‘E’. For example the sting “ACE”, means the CDL profiles ‘A’, ‘C’, and ‘E’ are considered when creating the channel matrices. The default is “ABCDE”, which means all CDL profiles are included.

  • delaySpread (float, tuple, or list) –

    Specifies the delay spread in nanoseconds. It can be one of the following:

    • If it is a tuple of the form (dsMin, dsMax), a random value is uniformly sampled between dsMin and dsMax for each channel matrix.

    • If it is a list of the form [\(d_1\), \(d_2\), …, \(d_n\)], for each channel matrix a random delay spread is picked from those specified in the list.

    • If it is a single number, then the same delay spread is used for all channel matrices.

    The default is (10,500).

  • ueSpeed (float, tuple, or list) –

    Specifies the speed of the UE in meters per second. It can be one of the following:

    • If it is a tuple of the form (speedMin, speedMax), a random value is uniformly sampled between speedMin and speedMax for each channel matrix.

    • If it is a list of the form [\(s_1\), \(s_2\), …, \(s_n\)], for each channel matrix a random speed is picked from those specified in the list.

    • If it is a single number, then the same UE speed is used for all channel matrices.

    The default is (0,20).

  • ueDir (float, tuple, or list) –

    Specifies the direction of UE movement in the X-Y plane as an angle in degrees. It can be one of the following:

    • If it is a tuple of the form (dirMin, dirMax), a random value is uniformly sampled between dirMin and dirMax for each channel matrix.

    • If it is a list of the form [\(a_1\), \(a_2\), …, \(a_n\)], for each channel matrix a random UE direction is picked from those specified in the list.

    • If it is a single number, then the same UE direction is used for all channel matrices.

    The default is (0, 360).

  • kwargs (dict) –

    Here is a list of additional optional parameters that can be used to further customize the calculation of the channel matrices:

    normalizeGains:

    If the default value of True is used, the path gains are normalized.

    normalizeOutput:

    If the default value of True is used, the gains are normalized based on the number of receive antennas.

    filterLen:

    The length of the channel filter. The default is 16 sample.

    delayQuantSize:

    The size of the delay fraction quantization for the channel filter. The default is 64.

    stopBandAtten:

    The stop-band attenuation value (in dB) used by the channel filter. The default is 80 dB.

    txAntenna:

    The transmitter antenna, which is an instance of either the neoradium.antenna.AntennaPanel or neoradium.antenna.AntennaArray class. By default, it is a single antenna in a 1x1 antenna panel with vertical polarization.

    rxAntenna:

    The receiver antenna which is an instance of either the neoradium.antenna.AntennaPanel or neoradium.antenna.AntennaArray class. By default, it is a single antenna in a 1x1 antenna panel with vertical polarization.

    txOrientation:

    The orientation of the transmitter antenna. This is a list of 3 angle values in degrees for the bearing angle \(\alpha\), downtilt angle \(\beta\), and slant angle \(\gamma\). The default is [0,0,0]. Please refer to 3GPP TR 38.901, Section 7.1.3 for more information.

    rxOrientation:

    The orientation of the receiver antenna. This is a list of 3 angle values in degrees for the bearing angle \(\alpha\), downtilt angle \(\beta\), and slant angle \(\gamma\). The default is [0,0,0]. Please refer to 3GPP TR 38.901, Section 7.1.3 for more information.

    seed:

    The seed used to generate CDL channel matrices. The default value is None, indicating that this channel model uses the NeoRadium’s global random generator. In this case the results are not reproducible.

    carrierFreq:

    The carrier frequency of the CDL channel model in Hz. The default is 3.5 GHz.

    kFactor:

    The K-Factor (in dB) used for scaling. The default is None. If not specified (kFactor=None), K-factor scaling is disabled.

    xPolPower:

    The cross-polarization Power in dB. The default is 10db. For more details please refer to “Step 3” in 3GPP TR 38.901, Section 7.7.1.

    angleScaling:

    The Angle Scaling parameters. If specified, it must be a tuple of 2 numpy arrays.

    The first item specifies the mean values for angle scaling. It’s a 1-D NumPy array containing four values for: the Azimuth angle of Departure, Azimuth angle of Arrival, Zenith angle of Departure, and Zenith angle of Arrival.

    The second item specifies the RMS angle spread values. It is a 1-D numpy array containing four RMS values for: the Azimuth angle of Departure, Azimuth angle of Arrival, Zenith angle of Departure, and Zenith angle of Arrival. For more information, please refer to Angle Scaling.

    If this value is set to None (the default), the Angle Scaling is disabled.

Return type:

ChanGen, a generator object that is used to generate channel matrices.

Example:

# First create a carrier object with 25 PRBs and 15 kHz subcarrier spacing
carrier = Carrier(startRb=0, numRbs=25, spacing=15)

# Now create the generator
chanGen = CdlChannel.getChanGen(1000, carrier.curBwp,       # Number of channels and bandwidth part
                                profiles="ABCDE",           # Randomly pick a CDL profile
                                delaySpread=(10,500),       # Uniformly sample between 10 and 500 ns
                                ueSpeed=(5,20),             # Uniformly sample between 5 and 20 m/s
                                ueDir=[45, 135, 225, 315],  # Randomly pick one of these UE directions
                                carrierFreq=4e9,            # Carrier frequency
                                txAntenna=AntennaPanel([2,4], polarization="x"),  # 16 TX antennas
                                rxAntenna=AntennaPanel([1,2], polarization="x"),  # 4 RX antennas
                                seed=123)

# Create the channel matrices
allChannels = np.stack([chan for chan in chanGen])
print(f"Shape of 'allChannels': {allChannels.shape}")       # Prints (1000, 14, 300, 4, 16)

TDL Channel Model

This module implements the TdlChannel class, which encapsulates the functionality of the Tapped Delay Line (TDL) channel model

class neoradium.tdl.TdlChannel(bwp, profile='A', **kwargs)

This class implements the Tapped Delay Line (TDL) channel model based on 3GPP TR 38.901. It is derived from the ChannelModel class.

According to 3GPP TR 38.901, TDL models are mostly used for simplified evaluations, typically for non-MIMO cases. They are defined for the frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz.

Although the TDL model is defined primarily for non-MIMO use cases, this implementation does support MIMO configurations. However, unlike CdlChannel, this model does not work with the antenna classes AntennaPanel or AntennaArray. For TDL channel models, you just need to specify the number of transmit and receive antennas.

The typical use case involves instantiating a TdlChannel object and then calling functions such as getChannelMatrix(), applyToSignal(), applyToGrid(). Refer to the notebook TDL Channel for an example of using this class.

Parameters:

  • bwp (BandwidthPart) – The bandwidth part object used by the channel model to create channel matrices.

  • profile (str or None) –

    The TDL profile name. It can be one of the following:

    • The TDL profile which is one of ‘A’, ‘B’, ‘C’, ‘D’, ‘E’.

    • A string that specifies both profile and delay spread according to 3GPP TS 38.101-4: ‘A30’, ‘B100’, ‘C60’, ‘C300’, ‘D30’. The numbers here are the delay spread in nano seconds. Note that the delay spread specified here overrides any value passed to the delaySpread parameter (See ChannelModel)

    • A string that specifies profile, delay spread, and doppler shift according to 3GPP TS 38.101-4 Table B.2.2-1 and 3GPP TS 38.101-4 Table B.2.2-2. This includes ‘A30-5’, ‘A30-10’, ‘B100-400’, ‘C300-100’, ‘C300-600’, ‘C300-1200’, for FR1, and ‘A30-35’, ‘A30-75’, ‘A30-300’, ‘C60-300’, ‘D30-75’ for FR2. Note that the delay spread and doppler shift specified here overrides any values passed to the delaySpread and dopplerShift parameters (See ChannelModel)

    • None can be used to completely customize the TDL channel. See Customizing TDL Model below for more information.

  • kwargs (dict) –

    Here’s a list of additional optional parameters that can be used to further customize this channel model:

    normalizeGains:

    A boolean flag. The default value is True, indicating that the path gains are normalized before they are applied to the signals.

    normalizeOutput:

    A boolean flag. The default value is True, indicating that the gains are normalized based on the number of receive antennas.

    txDir:

    A string that represents the transmission direction, which can be either “Downlink” or “Uplink”. By default, it is set to “Downlink”.

    filterLen:

    The length of the channel filter. The default is 16 samples.

    delayQuantSize:

    The delay fraction quantization size of the channel filter. The default is 64.

    stopBandAtten:

    The stop-band attenuation value (in dB) used by the channel filter. The default is 80 dB.

    seed:

    The seed used by the random functions in the channel model. Setting this to a fixed value ensures that the channel model generates repeatable results. The default value is None, indicating that this channel model uses the NeoRadium’s global random generator.

    dopplerShift:

    The maximum Doppler shift in Hertz. The default value is 40 Hertz, which corresponds to a speed of approximately 10 kilometers per hour. A value of zero makes the channel model static. For trajectory-based channel models, this value is automatically assigned based on the maximum trajectory speed.

    carrierFreq:

    The carrier frequency of the channel model in Hz. The default is 3.5 GHz.

    delaySpread:

    The delay spread in nanoseconds. The default is 30 ns. It can also be a string containing one of the values in following table (See 3GPP TR 38.901 table 7.7.3-1)

    Delay Spread str

    Delay spread

    ’VeryShort’

    10 ns

    ’Short’

    30 ns

    ’Nominal’

    100 ns

    ’Long’

    300 ns

    ’VeryLong’

    1000 ns
    txAntennaCount:

    The number of transmitter antennas. Isotropic antenna elements are assumed. The default is 1.

    rxAntennaCount:

    The number of receiver antennas. Isotropic antenna elements are assumed. The default is 1.

    mimoCorrelation:

    Specifies the relative amount of MIMO correlation in MIMO use cases. It can be one of ‘Low’ (default), ‘Medium’, ‘MediumA’, ‘MediumB’, and ‘High’. See Calculating Correlation Matrix for more information.

    polarization:

    The polarization type for the MIMO use cases. It can be one of ‘CoPolar’(default) or ‘CrossPolar’. See Calculating Correlation Matrix for more information.

    correlationMatrix:

    The correlation matrix. By default this implementation calculates the correlation matrix based on 3GPP standard using the given parameters such as mimoCorrelation and polarization. However you can specify your own correlation matrix using this parameter. See Calculating Correlation Matrix for more information.

    kFactor:

    The K-Factor (in dB) used for scaling. The default is None. If not specified (kFactor=None), K-factor scaling is disabled.

    sosType:

    The TDL model uses the Sum-of-Sinusoids method to calculate the channel gains. This implementation supports 2 different variations of this technique:

    sosNumSins:

    The number of Sinusoids used in the Sum-of-Sinusoids algorithm. The default is 32.

    pathDelays:

    Use this to customize or override the path delays which by default are set based on the TDL channel model as defined in 3GPP TR 38.901 Tables 7.7.2-1 to 7.7.2-5. You don’t need to specify this parameter for most use cases. See Customizing TDL Model below for more information.

    pathPowers:

    Use this to customize or override the path powers which by default are set based on the TDL channel model as defined in 3GPP TR 38.901 Tables 7.7.2-1 to 7.7.2-5. You don’t need to specify this parameter for most use cases. See Customizing TDL Model below for more information.

    kFactorLos:

    The K-Factor ratio (in dB) for the LOS tap (1st tap). This value by default is set to (pathPowers[0]-pathPowers[1]).

    hasLos:

    Use this to customize or override the hasLos property of this channel model which by default is set based on the TDL channel model as defined in 3GPP TR 38.901. You don’t need to specify this parameter for most use cases. See Customizing TDL Model below for more information.

Other Properties:

All of the parameters mentioned above are directly available. Here is a list of additional properties:

coherenceTime:

The Coherence time of the channel model in seconds. This is calculated based on the dopplerShift parameter.

sampleRate:

The sample rate used by this channel model. For 3GPP standard, this is set to 30,720,000 samples per second.

nrNt:

A tuple of the form (nr,nt), where nr and nt are the number of receiver and transmitter antenna elements, respectively. For TDL models, nr and nt are the same as rxAntennaCount and txAntennaCount parameters.

Calculating Correlation Matrix:

For MIMO use cases, by default, this class uses the parameters such as mimoCorrelation and polarization to calculate the correlation matrix based on the 3GPP standard documents. This implementation is based on the following documents:

  • 3GPP TS 38.101-4 Section B.2.3.2.1

  • 3GPP TS 38.101-4 Tables B.2.3.1.2-1 and B.2.3.2.2-1

  • 3GPP TS 38.104 Tables G.2.3.1.2-1 and G.2.3.2.3-1

Customizing TDL Model:

There are two different ways to customize the TDL model:

  1. You can choose one of the predefined TDL profiles (See the profile parameter above) and then modify the parameters of the model by passing in additional information. For example you can choose ‘B100-400’ for profile and then pass your own path delays to override the path delays specified in the standard.

  2. You can also define your own model completely from scratch. You first pass None for the profile parameter and then specify all channel model parameters. Note that you must specify at least the following parameters in this case:

    • pathDelays

    • pathPowers

    • hasLos

    You can also optionally specify the following values:

    • kFactorLos (defaults to pathPowers[0]-pathPowers[1])

    Also note that if your channel model contains a LOS tap, it must be the first tap in the lists, and the hasLos parameter should be set to True.

print(indent=0, title=None, getStr=False)

Prints the properties of this CDL channel model object.

Parameters:

  • indent (int) – The number of indentation characters.

  • title (str or None) – If specified, it serves as the title for the printed information. If None (the default), an automatic title is generated based on the channel model parameters.

  • getStr (Boolean) – If True, returns a text string instead of printing it.

Returns:

If the getStr parameter is True, then this function returns the information in a text string. Otherwise, nothing is returned.

Return type:

None or str

restart(restartRanGen=False, applyToBwp=True)

This method first re-initializes the random object if a seed was provided to this channel model and the restartRanGen parameter is set to True. Subsequently, it invokes the base class’s restart() method.

Parameters:

  • restartRanGen (Boolean) – If a seed was not provided to this channel model, this parameter is ignored. Otherwise, if restartRanGen is set to True, this channel model’s random generator is reset and if restartRanGen is False (default), the random generator is not reset. This means if restartRanGen is False, calling this function starts a new sequence of channel instances that are different from the sequence when the channel was instantiated.

  • applyToBwp (Boolean) – If set to True (the default), this function restarts the Bandwidth Part associated with this channel model. Otherwise, the Bandwidth Part’s state remains unchanged.

Trajectory Channel Model

This module implements the TrjChannel class, which encapsulates the information and functionality for creating trajectory-based, spatially and temporally consistent sequences of channels. They can be used in simulations to study the evolution of the channel over time and its effect on communication. These channel models can also be used to generate datasets for sequential deep learning models, such as LSTMs and Transformers.

class neoradium.trjchan.TrjPoint(xyz=[0, 0, 0], hasLos=-1, pathInfo=array([], shape=(0, 8), dtype=float64), bsDist=0, pathLoss=0, speed=array([0., 0., 0.]), sampleNo=0)

This class encapsulates the spatial and temporal information at each point along a trajectory. A Trajectory object comprises an ordered set of TrjPoint objects. This class is also used by the DeepMimoData class to store the multipath information obtained through ray-tracing at each point on the grid in a specified DeepMIMO scenario.

Note

You typically don’t create instances of this class directly. However, you can obtain TrjPoint instances from a Trajectory or a DeepMimoData object. This documentation may be helpful if you want to implement additional sources of trajectories beyond the DeepMIMO framework currently implemented in the DeepMimoData class.

Parameters:

  • xyz (list) – A list of three floating-point numbers giving the 3-D coordinates of this TrjPoint in meters. The default is [0, 0, 0].

  • hasLos (int) –

    An integer that can take on any of the following values:

    1:

    Means there is a line-of-sight (LOS) path between the UE at this TrjPoint and the base station.

    0:

    Means there is no line-of-sight (LOS) path between the UE at this TrjPoint and the base station.

    -1:

    Means there is no path between the UE at this TrjPoint and the base station. (Total Blockage)

  • pathInfo (numpy array) –

    An n × 8 matrix, where n is the number of paths. It contains the multipath information at this TrjPoint. The default is an empty numpy array. The eight parameters for each path are stored in this matrix as follows:

    0:

    Path phase angle in degrees.

    1:

    Path delay in nanoseconds.

    2:

    Path gain in dB.

    3:

    Azimuth angle Of Arrival (AOA) in degrees.

    4:

    Zenith angle Of Arrival (ZOA) in degrees.

    5:

    Azimuth angle Of Departure (AOD) in degrees.

    6:

    Zenith angle Of Departure (ZOD) in degrees.

    7:

    The path interactions (See the bounces parameter below). For each path, it is an integer that can take values of 0, -1, or a positive number. A value of 0 indicates a line-of-sight path. A value of -1 typically indicates the path interaction information is not available, for example when older DeepMIMO scenario files are used. If the value is positive, the number of digits specifies the number of interactions the path had on its journey from the transmitter to the receiver. Each digit represents a specific type of interaction:

    1:

    Reflection

    2:

    Diffraction

    3:

    Scattering

    4:

    Transmission

  • bsDist (float) – The distance between this TrjPoint and the base station in meters. The default is zero.

  • speed (numpy array) – The 3D vector of linear speed at this TrjPoint in meters per second (m/s).

  • sampleNo (int) – The time when the UE arrives at this TrjPoint as a number of samples from the start of Trajectory. This is based on 3GPP sample rate of 30,720,000 samples per second.

Other Properties:

numPaths:

The number of paths between the UE at this TrjPoint and the base station.

numNlosPaths:

The number of Non-Line-Of-Sight (NLOS) paths between the UE at this TrjPoint and the base station.

time:

The time (in seconds) when the user arrives at this TrjPoint. This read-only value is zero for the first TrjPoint of the Trajectory and increases based on the speed and the distance between the points along the trajectory.

linearSpeed:

UE’s linear speed at this TrjPoint (Read-only).

phases:

The phase angles in degrees for all paths or None if there is are paths.

delays:

The delay in nanoseconds for all paths or None if there are no paths.

powers:

The gain in dB for all NLOS paths or None if there are no paths.

aoas:

The Azimuth angle Of Arrival (AOA) in degrees for all paths or None if there are no paths.

zoas:

The Zenith angle Of Arrival (ZOA) in degrees for all paths or None if there are no paths.

aods:

The Azimuth angle Of Departure (AOD) in degrees for all paths or None if there are no paths.

zods:

The Zenith angle Of Departure (ZOD) in degrees for all paths or None if there are no paths.

bounces:

The path interaction information for all paths or None if there are no paths. See the explanation of the path interactions for the pathInfo parameter above.

losPhase:

The LOS path’s phase angle in degrees or None if there is no LOS path.

losDelay:

The LOS path’s delay in nanoseconds or None if there is no LOS path.

losPower:

The LOS path’s gain in dB or None if there is no LOS path.

losAoa:

The LOS path’s Azimuth angle Of Arrival (AOA) in degrees or None if there is no LOS path.

losZoa:

The LOS path’s Zenith angle Of Arrival (ZOA) in degrees or None if there is no LOS path.

losAod:

The LOS path’s Azimuth angle Of Departure (AOD) in degrees or None if there is no LOS path.

losZod:

The LOS path’s Zenith angle Of Departure (ZOD) in degrees or None if there is no LOS path.

nlosPhases:

The phase angles in degrees for all NLOS paths or None if there are no NLOS paths.

nlosDelays:

The delay in nanoseconds for all NLOS paths or None if there are no NLOS paths.

nlosPowers:

The gain in dB for all NLOS paths or None if there are no NLOS paths.

nlosAoas:

The Azimuth angle Of Arrival (AOA) in degrees for all NLOS paths or None if there are no NLOS paths.

nlosZoas:

The Zenith angle Of Arrival (ZOA) in degrees for all NLOS paths or None if there are no NLOS paths.

nlosAods:

The Azimuth angle Of Departure (AOD) in degrees for all NLOS paths or None if there are no NLOS paths.

nlosZods:

The Zenith angle Of Departure (ZOD) in degrees for all NLOS paths or None if there are no NLOS paths.

print(indent=0, title='TrjPoint Properties:', getStr=False)

Prints the properties of this class.

Parameters:

  • indent (int) – Used internally to adjust the indentation of the printed info.

  • title (str) – The title used for the information. By default the text “TrjPoint Properties:” is used.

  • getStr (boolean) – If True, returns a text string instead of printing it.

Returns:

If “getStr” is true, this function returns a text string containing the information about the properties of this class. Otherwise, nothing is returned (default).

Return type:

str or None

class neoradium.trjchan.Trajectory(points, carrierFreq)

This class maintains a collection of TrjPoint objects that collectively form a UE trajectory. Additionally, it offers the capability to update its internal state as the UE moves along the trajectory.

The trajectory objects are typically generated using a Ray-Tracing framework, such as DeepMIMO. For instance, the methods interactiveTrajPoints() and trajectoryFromPoints() can be used to interactively define a trajectory. You can also use the getRandomTrajectory() method to create a random trajectory based on the provided Ray-Tracing Scenario, along with other spatial and temporal information about the desired trajectory configuration. Please refer to the notebooks Working with DeepMIMO Scenarios and Animating Trajectories for examples of using this class.

Parameters:

  • points (list) – A list of TrjPoint objects defining this trajectory.

  • carrierFreq (float) – The carrier frequency (in Hz) of the communication between the UE and the base station while traveling along this trajectory. This is typically specified by the ray-tracing scenario used to create this trajectory.

Other Properties:

curIdx:

The current index in the list of TrjPoint objects. This starts at zero and is incremented as the UE travels along the trajectory.

minPaths, avgPaths, maxPaths:

Statistics representing the minimum, average, and maximum number of paths between the UE and the base station for all points along this trajectory.

numPoints:

A read-only property that returns the total number of points on this trajectory.

numLOS:

A read-only property that returns the total number of points along this trajectory with a Line-Of-Sight (LOS) path between the UE and the base station.

numBlockage:

A read-only property that returns the total number of points along this trajectory with total blockage (i.e., no paths between the UE and the base station).

cur:

A read-only property returning current TrjPoint on the trajectory. This is the TrjPoint object at index curIdx.

remainingPoints:

A read-only property returning the remaining points from cur to the end of trajectory (including cur).

time:

A read-only property returning the total travel time (in seconds) along this trajectory. This is the time of the last point on the trajectory (trajectory[-1].time).

totalDist:

A read-only property returning the total distance (in meters) traveled along this trajectory.

Indexing:

This class supports direct indexing with TrjPoint objects. For example:

deepMimoData = DeepMimoData("O1_3p5B", baseStationId=3)
carrier = Carrier(startRb=0, numRbs=25, spacing=15)  # Carrier with 25 Resource Blocks, 15 kHz spacing
xyBounds = np.array([[250, 300], [270, 400]])        # [[minX, minY], [maxX, maxY]]
trajLen = 100
segLen = 2
traj = deepMimoData.getRandomTrajectory(xyBounds, segLen, carrier.curBwp, trajLen, trajDir="-X")
for point in traj[:5]:   # print the times of the first 5 points on the trajectory
    print(point.time)
print(traj[-1].time)     # print the time of the last point on the trajectory

Iterating through points:

This class has a generator function (__iter__) which makes it easier to use it in a loop. For example, the following code finds the point on the trajectory that is farthest from the base station.

deepMimoData = DeepMimoData("O1_3p5B", baseStationId=3)
carrier = Carrier(startRb=0, numRbs=25, spacing=15)  # Carrier with 25 Resource Blocks, 15 kHz spacing
xyBounds = np.array([[250, 300], [270, 400]])        # [[minX, minY], [maxX, maxY]]
trajLen = 100
segLen = 2
traj = deepMimoData.getRandomTrajectory(xyBounds, segLen, carrier.curBwp, trajLen, trajDir="-X")

farPoint = None
maxDist = 0
for point in traj:
    if point.bsDist>maxDist:
        farPoint = point
        maxDist = point.bsDist
print(farPoint)

TrjPoint Redirection:

The properties phases, delays, powers, aoas, zoas, aods, zods, bounces, losPhase, losDelay, losPower, losAoa, losZoa, losAod, losZod, nlosPhases, nlosDelays, nlosPowers, nlosAoas, nlosZoas, nlosAods, nlosZods, hasLos, numPaths*, and numNlosPaths are redirected to the corresponding property in the TrjPoint class for the current point (cur). For example traj.losZod is equivalent to traj.cur.losZod.

restart()

This function resets the current trajectory point to the starting point.

goNext()

This function advances current point in this trajectory.

draw()

This visualization function draws this trajectory object using the matplotlib library. The starting point of the trajectory is shown with a small red circle.

Example:

deepMimoData = DeepMimoData("O1_3p5B", baseStationId=3)
carrier = Carrier(startRb=0, numRbs=25, spacing=15)  # Carrier with 25 Resource Blocks, 15 kHz spacing
xyBounds = np.array([[250, 300], [270, 400]])        # [[minX, minY], [maxX, maxY]]
trajLen = 100
segLen = 2
traj = deepMimoData.getRandomTrajectory(xyBounds, segLen, carrier.curBwp, trajLen, trajDir="-X")
traj.draw()
../../_images/Trajectory.png
print(indent=0, title='Trajectory Properties:', getStr=False)

Prints the properties of this class.

Parameters:

  • indent (int) – Used internally to adjust the indentation of the printed info.

  • title (str) – The title used for the information. By default the text “Trajectory Properties:” is used.

  • getStr (boolean) – If this is True, the function returns a text string instead of printing the info. Otherwise when this is False (default) the function prints the information.

Returns:

If “getStr” is true, this function returns a text string containing the information about the properties of this class. Otherwise, nothing is returned (default).

Return type:

str or None

class neoradium.trjchan.TrjChannel(bwp, trajectory, **kwargs)

This class implements a trajectory-based channel model that can generate spatially and temporally consistent sequences of channel information. It creates channel instances based on the movement of users along a Trajectory. The TrjChannel class has been implemented based on 3GPP TR 38.901 Section 8 with the following additional assumptions:

  • We assume only one frequency bin (\(K_B=1\)) and derive the path power \(P_{l_{RT}}^{RT,real}\) from the multipath information for each path. As explained in “Step 2” of 3GPP TR 38.901 Section 8.4, this implies that the bandwidth \(B\) must be lower than \(c/D\) Hz, where \(c\) is the speed of light and \(D\) is the maximum antenna aperture in either azimuth or elevation.

  • We skip Steps 4 to 10 in 3GPP TR 38.901 Section 8.4. In essence, we refrain from generating random clusters and rays and only use the deterministic paths calculated through ray-tracing. Consequently, the number of paths is equivalent to the number of clusters, and each cluster contains a single ray.

  • We use the same Cross-Polarization Ratio (XPR) for all paths. This value (\(\kappa^{RT}\)) is derived from the xPolPower parameter (\(X\)) using:

\[\kappa^{RT} = 10^{\frac X {10}}\]

  • For the initial phase values used to calculate the cross-polarization matrix, we opt for the phase values obtained from ray-tracing instead of generating random values (as outlined in “Step 12” of 3GPP TR 38.901 Section 8.4). Additionally, we employ the same phase value for all four initial phase angles: \(\Phi^{\theta \theta}\), \(\Phi^{\theta \phi}\), \(\Phi^{\phi \theta}\), and \(\Phi^{\phi \phi}\). This approach is expected to enhance spatial consistency when generating sequences of channels, which is the main goal of this channel model.

All API functions used in most typical use cases are explained in the documentation of ChannelModel class.

The typical use case involves creating a trajectory (using interactiveTrajPoints() and trajectoryFromPoints(), or getRandomTrajectory()), using it to obtain a TrjChannel object, and then calling functions such as getChannelMatrix(), applyToSignal(), applyToGrid().

Please refer to the notebook Using the Trajectory-based Channel Model for an example of using this class.

Parameters:

  • bwp (BandwidthPart) – The bandwidth part object used by the channel model to construct channel matrices.

  • trajectory (Trajectory) – The trajectory along which the channels are created by this channel model. This object contains multipath information that is used to create sequences of channels.

  • kwargs (dict) –

    Here’s a list of additional optional parameters that can be used to further customize this channel model:

    normalizeGains:

    If the default value of True is used, the path gains are normalized before being applied to the signals.

    normalizeOutput:

    If the default value of True is used, the gains are normalized based on the number of receive antennas.

    normalizeDelays:

    If the default value of True is used, the delays are normalized as specified in “Step 3” of 3GPP TR 38.901 section 8.4. Otherwise, the original delays obtained from ray-tracing are used.

    filterLen:

    The length of the channel filter. The default is 16 sample.

    delayQuantSize:

    The size of delay fraction quantization for the channel filter. The default is 64.

    stopBandAtten:

    The Stop-band attenuation value (in dB) used by the channel filter. The default is 80dB.

    txAntenna:

    The transmitter antenna, which is an instance of AntennaElement, AntennaPanel, or AntennaArray class. If not specified, a single antenna element is automatically created by default.

    rxAntenna:

    The receiver antenna, which is an instance of AntennaElement, AntennaPanel, or AntennaArray class. If not specified, a single antenna element is automatically created by default.

    txOrientation:

    The orientation of transmitter antenna. This is a list of 3 angle values in degrees for the bearing angle \(\alpha\), downtilt angle \(\beta\), and slant angle \(\gamma\). The default is [0,0,0]. Please refer to 3GPP TR 38.901 Section 7.1.3 for more information.

    rxOrientation:

    The orientation of receiver antenna. This is a list of 3 angle values in degrees for the bearing angle \(\alpha\), downtilt angle \(\beta\), and slant angle \(\gamma\). The default is [0,0,0]. Please refer to 3GPP TR 38.901 Section 7.1.3 for more information.

    xPolPower:

    The Cross-Polarization Power in dB. The default is 10db. It is defined as \(X=10 log_{10} \kappa^{RT}\) where \(\kappa^{RT}\) is the Cross-Polarization Ratio (XPR). In current implementation this value is used for all paths.

Other Properties:

nrNt:

A tuple of the form (nr,nt), where nr and nt are the number receiver and transmitter antenna elements, respectively.

pathPowers:

This read-only property returns the path powers for all paths between the base station and the UE at current position on the current trajectory.

pathDelays:

This read-only property returns the path delays for all paths between the base station and the UE at current position on the trajectory. If the parameter normalizeDelays is True, the delays from the ray-tracing are normalized according to “Step 3” in 3GPP TR 38.901 Section 8.4. Note that the delay normalization (if enabled) is only applied to this property; the redirected properties “delays”, “losDelay”, and “nlosDelays” always keep the original delay values from the ray-tracing.

TrjPoint Redirection:

The properties phases, delays, powers, aoas, zoas, aods, zods, bounces, losPhase, losDelay, losPower, losAoa, losZoa, losAod, losZod, nlosPhases, nlosDelays, nlosPowers, nlosAoas, nlosZoas, nlosAods, nlosZods, hasLos, numPaths*, and numNlosPaths are redirected to the corresponding property in the TrjPoint class for the current point (cur) in the trajectory. For example channel.losZod is equivalent to channel.trajectory.cur.losZod.

restart(restartRanGen=False, applyToBwp=True)

This method first resets the current trajectory to its starting position and then calls the base class restart().

Parameters:

  • restartRanGen (Boolean) – Ignored for the TrjChannel class as it uses deterministic information only.

  • applyToBwp (Boolean) – If set to True (the default), this function restarts the Bandwidth Part associated with this channel model. Otherwise, the Bandwidth Part’s state remains unchanged.

goNext(applyToBwp=True)

This method is called after each application of the channel to a signal. It updates the timing information of the trajectory and the channel model preparing it for the next application to the input signal. It is assumed that the channel is applied to a single slot of the signal at each application (either in the time or frequency domain).

Parameters:

applyToBwp (Boolean) – If set to True (the default), this function advances the timing state of the Bandwidth Part associated with this channel model. Otherwise, the Bandwidth Part’s state remains unchanged.

print(indent=0, title=None, getStr=False)

Prints the properties of this channel model object.

Parameters:

  • indent (int) – The number of indentation characters.

  • title (str or None) – If specified, it is used as a title for the printed information. If None (default), the text “TrjChannel Properties:” is used for the title.

  • getStr (Boolean) – If True, returns a text string instead of printing it.

Returns:

If the getStr parameter is True, then this function returns the information in a text string. Otherwise, nothing is returned.

Return type:

None or str

getPathGains()

Calculates the gains for Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths separately and combines the results before returning the gains between every RX/TX antenna pair, for every path, at every time instance.

Returns:

This function returns a 4-D complex tensor of shape L x Nr x Nt x Np, where L represents the number of time symbols, Nr and Nt indicate the number of receiver and transmitter antennas respectively, and Np denotes the number of paths between the base station and the UE at its current location along its trajectory.

Return type:

numpy array or None

getChanSeqGen(seqPeriod=1, seqLen=10, maxNumSeq=inf)

Returns a generator object that can generate sequences of channel matrices based on the given parameters.

Refer to the notebook Creating temporally and spatially consistent sequences of channel matrices for an example of using this method.

Parameters:

  • seqPeriod (int) – The sampling period of channel matrices along the trajectory. The default value of 1 means all channel matrices are included in the sequence. For instance, if this is set to 3, every other three channel matrices (i.e., every other three slots) are included in the sequence.

  • seqLen (int) – The length of the returned sequences. The default is 10.

  • maxNumSeq (int) – The maximum number of sequences to generate. By default, it is set to np.inf, indicating no additional limit for the number of sequences. In this case, the channel sequences are generated until the end of the trajectory.

Return type:

ChanSeqGen, a generator object that is used to generate sequences of channel matrices.