FlatFadingChannel#

class sionna.phy.channel.FlatFadingChannel(num_tx_ant: int, num_rx_ant: int, spatial_corr: sionna.phy.channel.spatial_correlation.SpatialCorrelation | None = None, return_channel: bool = False, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs)[source]#

Bases: sionna.phy.block.Block

Applies random channel matrices to a vector input and adds AWGN

This class combines GenerateFlatFadingChannel and ApplyFlatFadingChannel and computes the output of a flat-fading channel with AWGN.

For a given batch of input vectors \(\mathbf{x}\in\mathbb{C}^{K}\), the output is

\[\mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n}\]

where \(\mathbf{H}\in\mathbb{C}^{M\times K}\) are randomly generated flat-fading channel matrices and \(\mathbf{n}\in\mathbb{C}^{M}\sim\mathcal{CN}(0, N_o\mathbf{I})\) is an AWGN vector that is optionally added.

A SpatialCorrelation can be configured and the channel realizations optionally returned. This is useful to simulate receiver algorithms with perfect channel knowledge.

Parameters:

  • num_tx_ant (int) – Number of transmit antennas

  • num_rx_ant (int) – Number of receive antennas

  • spatial_corr (sionna.phy.channel.spatial_correlation.SpatialCorrelation | None) – Spatial correlation to be applied. Defaults to None.

  • return_channel (bool) – Indicates if the channel realizations should be returned. Defaults to False.

  • precision (Literal['single', 'double'] | 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:

  • x – [batch_size, num_tx_ant], torch.complex. Tensor of transmit vectors.

  • no – None (default) | torch.Tensor, torch.float. (Optional) noise power no per complex dimension. Will be broadcast to the shape of y. For more details, see AWGN.

Outputs:

  • y – [batch_size, num_rx_ant], torch.complex. Channel output.

  • h – [batch_size, num_rx_ant, num_tx_ant], torch.complex. Channel realizations. Will only be returned if return_channel==True.

Examples

import torch
from sionna.phy.channel import FlatFadingChannel

chn = FlatFadingChannel(num_tx_ant=4, num_rx_ant=16, return_channel=True)
x = torch.randn(32, 4, dtype=torch.complex64)
y, h = chn(x)
print(y.shape)
# torch.Size([32, 16])
print(h.shape)
# torch.Size([32, 16, 4])

Attributes

property spatial_corr: sionna.phy.channel.spatial_correlation.SpatialCorrelation | None#

Get/set spatial correlation to be applied

property generate: sionna.phy.channel.flat_fading_channel.GenerateFlatFadingChannel#

Access the internal GenerateFlatFadingChannel

property apply: sionna.phy.channel.flat_fading_channel.ApplyFlatFadingChannel#

Access the internal ApplyFlatFadingChannel