77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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from timeit import default_timer as timer
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from decoders import proximal, naive_soft_decision
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from utility import noise, simulations, encoders, codes
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def main():
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# used_code = "Hamming_7_4"
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# used_code = "Golay_24_12"
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# used_code = "BCH_31_16"
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# used_code = "BCH_31_21"
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used_code = "BCH_63_16"
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G = codes.Gs[used_code]
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H = codes.get_systematic_H(G)
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R = codes.get_systematic_R(G)
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# Define encoder and decoders
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encoder = encoders.Encoder(G)
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decoders = {
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# "naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H, R),
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"proximal_0_01": proximal.ProximalDecoder(H, R, K=100, gamma=0.01),
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"proximal_0_05": proximal.ProximalDecoder(H, R, K=100, gamma=0.05),
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"proximal_0_15": proximal.ProximalDecoder(H, R, K=100, gamma=0.15),
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}
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# Test decoders
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k, n = G.shape
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d = np.zeros(k) # All-zeros assumption
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SNRs = np.linspace(1, 8, 9)
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data = pd.DataFrame({"SNR": SNRs})
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start_time = timer()
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for decoder_name in decoders:
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decoder = decoders[decoder_name]
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_, BERs_sd = simulations.test_decoder(encoder=encoder,
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decoder=decoder,
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d=d,
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SNRs=SNRs,
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target_bit_errors=500,
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N_max=10000)
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data[f"BER_{decoder_name}"] = BERs_sd
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stop_time = timer()
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print(f"Elapsed time: {stop_time - start_time:2f}")
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# Plot results
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sns.set_theme()
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fig, axes = plt.subplots(1, 1)
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fig.suptitle("Bit-Error-Rates of various decoders")
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for decoder_name in decoders:
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ax = sns.lineplot(data=data, x="SNR", y=f"BER_{decoder_name}", label=f"{decoder_name}")
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ax.set_title(used_code)
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ax.set(yscale="log")
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ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
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ax.legend()
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plt.show()
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if __name__ == "__main__":
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main()
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