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