time_frequency_vector

sionna.phy.channel.utils.time_frequency_vector(num_samples: int, sample_duration: float, precision: str | None = None, device: str | None = None) → Tuple[torch.Tensor, torch.Tensor]

Compute the time and frequency vector for a given number of samples and duration per sample in normalized time unit.

>>> t = torch.linspace(-n_min, n_max, num_samples) * sample_duration
>>> f = torch.linspace(-n_min, n_max, num_samples) * 1/(sample_duration*num_samples)

Parameters:

• num_samples (int) – Number of samples

• sample_duration (float) – Sample duration in normalized time

• precision (str | 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.

Outputs:

• t – [num_samples], torch.float. Time vector.

• f – [num_samples], torch.float. Frequency vector.

Examples

from sionna.phy.channel import time_frequency_vector

t, f = time_frequency_vector(128, 1e-6)
print(t.shape, f.shape)
# torch.Size([128]) torch.Size([128])