67 lines
1.7 KiB
Python
67 lines
1.7 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 decoders import proximal
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from decoders import naive_soft_decision
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from decoders import channel
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from decoders import utility
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def main():
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# Hamming(7,4) code
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G = np.array([[1, 1, 1, 0, 0, 0, 0],
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[1, 0, 0, 1, 1, 0, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[1, 1, 0, 1, 0, 0, 1]])
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H = np.array([[1, 0, 1, 0, 1, 0, 1],
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[0, 1, 1, 0, 0, 1, 1],
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[0, 0, 0, 1, 1, 1, 1]])
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# Define encoder and decoders
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encoder = channel.Encoder(G)
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decoders = {"naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H),
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# "proximal": proximal.ProximalDecoder(H, K=100, gamma=0.01),
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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, 7, 9)
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data = pd.DataFrame({"SNR": SNRs})
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for decoder_name in decoders:
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decoder = decoders[decoder_name]
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_, BERs_sd = utility.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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data[f"BER_{decoder_name}"] = BERs_sd
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# Plot results
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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("Hamming(7,4) 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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