BlackmanWindow

class sionna.phy.signal.BlackmanWindow(normalize: bool = False, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs)

Bases: sionna.phy.signal.window.Window

Block for defining a Blackman window function.

The window function is applied through element-wise multiplication.

The Blackman window is defined by

\[w_n = a_0 - a_1 \cos \left( \frac{2 \pi n}{N} \right) + a_2 \cos \left( \frac{4 \pi n}{N} \right), 0 \leq n \leq N-1\]

where \(N\) is the window length, \(a_0 = \frac{7938}{18608}\), \(a_1 = \frac{9240}{18608}\), and \(a_2 = \frac{1430}{18608}\).

Parameters:

• 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. The 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 BlackmanWindow

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

Methods

build(input_shape: tuple) → None

Initialize the block based on the inputs’ shapes.

Subclasses can override this method to create tensors or sub-blocks whose sizes depend on the input shapes.

Parameters:

• *arg_shapes – Shapes of the positional arguments. Can be tuples (for tensors) or nested structures thereof (for lists/dicts of tensors).

• **kwarg_shapes – Shapes of the keyword arguments. Can be tuples (for tensors) or nested structures thereof (for lists/dicts of tensors).

• input_shape (tuple)