ba-thesis/sw/main.py

81 lines
2.2 KiB
Python

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
def main():
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
# 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,
N_max=2000)
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("Hamming(7,4) 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()