81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
"""This file contains various utility functions that can be used in combination with the decoders.
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"""
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import numpy as np
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import typing
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from tqdm import tqdm
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def _get_noise_amp_from_SNR(SNR: float, signal_amp: float = 1) -> float:
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"""Calculate the amplitude of the noise from an SNR and the signal amplitude
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:param SNR: Signal-to-Noise-Ratio in dB
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:param signal_amp: Signal Amplitude (linear)
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:return: Noise Amplitude (linear)
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"""
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SNR_linear = 10 ** (SNR / 10)
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noise_amp = (1 / np.sqrt(SNR_linear)) * signal_amp
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return noise_amp
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def add_awgn(c: np.array, SNR: float) -> np.array:
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"""Add Additive White Gaussian Noise to a data vector. As this function adds random noise to the input,
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the output changes, even if it is called multiple times with the same input.
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:param c: Binary vector representing the data to be transmitted
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:param SNR: Signal-to-Noise-Ratio in dB
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:return: Data vector with added noise
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"""
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noise_amp = _get_noise_amp_from_SNR(SNR, signal_amp=1)
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y = c + np.random.normal(scale=noise_amp, size=c.size)
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return y
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def count_bit_errors(d: np.array, d_hat: np.array) -> int:
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"""Count the number of wrong bits in a decoded codeword.
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:param d: Originally sent data
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:param d_hat: Received data
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:return: Number of bit errors
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"""
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return np.sum(d != d_hat)
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def test_decoder(decoder: typing.Any,
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c: np.array,
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SNRs: typing.Sequence[float] = np.linspace(1, 4, 7),
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N=10000) \
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-> typing.Tuple[np.array, np.array]:
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"""Calculate the Bit Error Rate (BER) for a given decoder for a number of SNRs.
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This function prints it's progress to stdout
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:param decoder: Instance of the decoder to be tested
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:param c: Codeword whose transmission is to be simulated
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:param SNRs: List of SNRs for which the BER should be calculated
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:param N: Number of iterations to perform for each SNR
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:return: Tuple of numpy arrays of the form (SNRs, BERs)
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"""
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BERs = []
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for SNR in tqdm(SNRs, desc="Calculating Bit-Error-Rates",
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position=0,
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}"):
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total_bit_errors = 0
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for n in tqdm(range(N), desc=f"Simulating for SNR = {SNR} dB",
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position=1,
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leave=False,
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}"):
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y = add_awgn(c, SNR)
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y_hat = decoder.decode(y)
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total_bit_errors += count_bit_errors(c, y_hat)
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total_bits = c.size * N
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BERs.append(total_bit_errors / total_bits)
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return np.array(SNRs), np.array(BERs)
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