tdl_freq_cov_mat

sionna.phy.ofdm.tdl_freq_cov_mat(model: str, subcarrier_spacing: float, fft_size: int, delay_spread: float, precision: Literal['single', 'double'] | None = None) → torch.Tensor

Compute the frequency covariance matrix of a TDL channel model.

The channel frequency covariance matrix \(\mathbf{R}^{(f)}\) of a TDL channel model is

\[\mathbf{R}^{(f)}_{u,v} = \sum_{\ell=1}^L P_\ell e^{-j 2 \pi \tau_\ell \Delta_f (u-v)}, 1 \leq u,v \leq M\]

where \(M\) is the FFT size, \(L\) is the number of paths for the selected TDL model, \(P_\ell\) and \(\tau_\ell\) are the average power and delay for the \(\ell^{\text{th}}\) path, respectively, and \(\Delta_f\) is the sub-carrier spacing.

Parameters:

• model (str) – TDL model ("A", "B", "C", "D", "E")

• subcarrier_spacing (float) – Sub-carrier spacing [Hz]

• fft_size (int) – FFT size

• delay_spread (float) – Delay spread [s]

• precision (Literal['single', 'double'] | None) – Precision used for internal calculations and outputs. If set to None, precision is used.

Examples

from sionna.phy.ofdm import tdl_freq_cov_mat

cov_mat = tdl_freq_cov_mat("A", 30e3, 64, 100e-9)
print(cov_mat.shape)
# torch.Size([64, 64])