CDL#

class sionna.phy.channel.tr38901.CDL(model: str, delay_spread: float, carrier_frequency: float, ut_array=None, bs_array=None, direction: str = 'downlink', ut_orientation: torch.Tensor | None = None, bs_orientation: torch.Tensor | None = None, ut_velocity: torch.Tensor | None = None, min_speed: float = 0.0, max_speed: float = 0.0, normalize_delays: bool = True, precision: str | None = None, device: str | None = None)[source]#

Bases: sionna.phy.channel.channel_model.ChannelModel

Clustered delay line (CDL) channel model from the 3GPP [TR38901] specification

The power delay profiles (PDPs) are normalized to have a total energy of one.

If a minimum speed and a maximum speed are specified such that the maximum speed is greater than the minimum speed, then UTs speeds are randomly and uniformly sampled from the specified interval for each link and each batch example.

The CDL model only works for systems with a single transmitter and a single receiver. The transmitter and receiver can be equipped with multiple antennas.

The channel coefficient generation is done following the procedure described in sections 7.7.1 and 7.7.3.

Parameters:

model (str) – CDL model to use. Must be "A", "B", "C", "D", or "E".
delay_spread (float) – RMS delay spread [s]. Ignored if normalize_delays is set to False.
carrier_frequency (float) – Carrier frequency [Hz]
ut_array – Antenna array used by the UTs. All UTs share the same antenna array configuration.
bs_array – Antenna array used by the BSs. All BSs share the same antenna array configuration.
direction (str) – Link direction. Must be "uplink" or "downlink".
ut_orientation (torch.Tensor | None) – Orientation of the UT. If set to None, [\(\pi\), 0, 0] is used. Shape [3] or [batch size, 3].
bs_orientation (torch.Tensor | None) – Orientation of the BS. If set to None, [0, 0, 0] is used. Shape [3] or [batch size, 3].
ut_velocity (torch.Tensor | None) – UT velocity vector [m/s]. If set to None, velocities are randomly sampled using min_speed and max_speed. Shape [3] or [batch size, 3].
min_speed (float) – Minimum speed [m/s]. Ignored if ut_velocity is not None. Defaults to 0.
max_speed (float) – Maximum speed [m/s]. Ignored if ut_velocity is not None. Defaults to 0.
normalize_delays (bool) – If set to True, the path delays are normalized such that the delay of the first path is zero. Defaults to True.
precision (str | None) – Precision used for internal calculations and outputs. If set to None, precision is used.
device (str | None) – Device for computation (e.g., "cpu", "cuda:0"). If None, device is used.

Inputs:

batch_size – int. Batch size.
num_time_steps – int. Number of time steps.
sampling_frequency – float. Sampling frequency [Hz].

Outputs:

a – [batch size, num_rx = 1, num_rx_ant, num_tx = 1, num_tx_ant, num_paths, num_time_steps], torch.complex. Path coefficients.
tau – [batch size, num_rx = 1, num_tx = 1, num_paths], torch.float. Path delays [s].

Examples

The following code snippet shows how to setup a CDL channel model assuming an OFDM waveform:

from sionna.phy.channel.tr38901 import Antenna, AntennaArray, CDL

# Antenna array configuration for the transmitter and receiver
bs_array = AntennaArray(
    antenna=Antenna(pattern="38.901", polarization="dual"),
    num_rows=4,
    num_cols=4,
)
ut_array = AntennaArray(
    antenna=Antenna(pattern="omni", polarization="single"),
    num_rows=1,
    num_cols=1,
)

# CDL channel model
cdl = CDL(
    model="A",
    delay_spread=300e-9,
    carrier_frequency=3.5e9,
    ut_array=ut_array,
    bs_array=bs_array,
    direction="uplink",
)

# Generate channel impulse response
a, tau = cdl(batch_size=64, num_time_steps=100, sampling_frequency=1e6)

Notes

The following tables from [TR38901] provide typical values for the delay spread.

Model	Delay spread [ns]
Very short delay spread	\(10\)
Short delay spread	\(30\)
Nominal delay spread	\(100\)
Long delay spread	\(300\)
Very long delay spread	\(1000\)

Delay spread [ns]		Frequency [GHz]
Delay spread [ns]		2	6	15	28	39	60	70
Indoor office	Short delay profile	20	16	16	16	16	16	16
	Normal delay profile	39	30	24	20	18	16	16
	Long delay profile	59	53	47	43	41	38	37
UMi Street-canyon	Short delay profile	65	45	37	32	30	27	26
	Normal delay profile	129	93	76	66	61	55	53
	Long delay profile	634	316	307	301	297	293	291
UMa	Short delay profile	93	93	85	80	78	75	74
	Normal delay profile	363	363	302	266	249	228	221
	Long delay profile	1148	1148	955	841	786	720	698
RMa / RMa O2I	Short delay profile	32	32	N/A	N/A	N/A	N/A	N/A
	Normal delay profile	37	37	N/A	N/A	N/A	N/A	N/A
	Long delay profile	153	153	N/A	N/A	N/A	N/A	N/A
UMi / UMa O2I	Normal delay profile	242
UMi / UMa O2I	Long delay profile	616

Methods

allocate_for_batch_size(batch_size: int, num_time_steps: int = 100) → None[source]#

Pre-allocate all tensors for CUDA graph compatibility.

Must be called before using with torch.compile(mode=”max-autotune”) or CUDA graphs.

Parameters:

batch_size (int) – Batch size to allocate for
num_time_steps (int) – Number of time steps to allocate for

CDL#

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