CustomWindow

class sionna.phy.signal.CustomWindow(coefficients: torch.Tensor | numpy.ndarray, normalize: bool = False, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs)

Bases: sionna.phy.signal.window.Window

Block for defining a custom window function.

The window function is applied through element-wise multiplication.

Parameters:

• coefficients (torch.Tensor | numpy.ndarray) – Window coefficients with shape [N]

• normalize (bool) – If True, the window is normalized to have unit average power per coefficient. 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. If None, device is used.

Inputs:

x – […, N], torch.complex or torch.float. Input to which the window function is applied. The window function is applied along the last dimension. The length of the last dimension N must be the same as the length of the window function.

Outputs:

y – […, N], torch.complex or torch.float. Output of the windowing operation.

Examples

import torch
from sionna.phy.signal import CustomWindow

coefficients = torch.hann_window(64)
window = CustomWindow(coefficients)
x = torch.randn(32, 64)
y = window(x)
print(y.shape)
# torch.Size([32, 64])