34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
"""Utility functions relating to noise and SNR calculations."""
|
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
def get_noise_variance_from_SNR(SNR: float, n: int, k: int) -> float:
|
|
"""Calculate the variance of the noise from an SNR and the signal amplitude.
|
|
|
|
:param SNR: Signal-to-Noise-Ratio in dB (E_b/N_0)
|
|
:param n: Length of a codeword of the used code
|
|
:param k: Length of a dataword of the used code
|
|
:return: Variance of the noise
|
|
"""
|
|
SNR_linear = 10 ** (SNR / 10)
|
|
variance = 1 / (2 * (k/n) * SNR_linear)
|
|
|
|
return variance
|
|
|
|
|
|
def add_awgn(c: np.array, SNR: float, n: int, k: int) -> np.array:
|
|
"""Add Additive White Gaussian Noise to a data vector. As this function adds random noise to
|
|
the input, the output changes, even if it is called multiple times with the same input.
|
|
|
|
:param c: Binary vector representing the data to be transmitted
|
|
:param SNR: Signal-to-Noise-Ratio in dB
|
|
:param n: Length of a codeword of the used code
|
|
:param k: Length of a dataword of the used code
|
|
:return: Data vector with added noise
|
|
"""
|
|
noise_var = get_noise_variance_from_SNR(SNR, n, k)
|
|
y = c + np.sqrt(noise_var) * np.random.normal(size=c.size)
|
|
return y
|