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Fixed SNR amplitude; Fixed BER calculation

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This commit is contained in:
Andreas Tsouchlos 2022-11-07 00:32:54 +01:00
parent 74ee1cc4db
commit be6ded2162
2 changed files with 9 additions and 6 deletions
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10
sw/decoders/utility.py
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@ -19,15 +19,16 @@ def _get_noise_amp_from_SNR(SNR: float, signal_amp: float = 1) -> float:
return noise_amp
def add_awgn(c: np.array, SNR: float) -> np.array:
def add_awgn(c: np.array, SNR: float, signal_amp: float = 1) -> np.array:
"""Add Additive White Gaussian Noise to a data vector. As this function adds random noise to the input,
the output changes, even if it is called multiple times with the same input.
:param c: Binary vector representing the data to be transmitted
:param SNR: Signal-to-Noise-Ratio in dB
:param signal_amp: Amplitude of the signal. Used for the noise amplitude calculation
:return: Data vector with added noise
"""
noise_amp = _get_noise_amp_from_SNR(SNR, signal_amp=1)
noise_amp = _get_noise_amp_from_SNR(SNR, signal_amp=signal_amp)
y = c + np.random.normal(scale=noise_amp, size=c.size)
return y
@ -66,6 +67,7 @@ def test_decoder(decoder: typing.Any,
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}"):
total_bit_errors = 0
total_bits = 0
for n in tqdm(range(N_max), desc=f"Simulating for SNR = {SNR} dB",
position=1,
@ -74,15 +76,15 @@ def test_decoder(decoder: typing.Any,
# TODO: Is this a valid simulation? Can we just add AWGN to the codeword, ignoring and modulation and (
# e.g. matched) filtering?
y = add_awgn(x, SNR)
y = add_awgn(x, SNR, signal_amp=(1 / np.sqrt(2)))
y_hat = decoder.decode(y)
total_bit_errors += count_bit_errors(c, y_hat)
total_bits += c.size
if total_bit_errors >= target_bit_errors:
break
total_bits = c.size * N_max
BERs.append(total_bit_errors / total_bits)
return np.array(SNRs), np.array(BERs)

5
sw/main.py
View File

@ -21,7 +21,7 @@ def main():
# Test decoder
d = np.array([0, 1, 0, 1])
d = np.array([0, 1, 1, 1])
c = np.dot(G.transpose(), d) % 2
print(f"Simulating with c = {c}")
@ -33,7 +33,8 @@ def main():
ax = sns.lineplot(data=data, x="SNR", y="BER")
ax.set(yscale="log")
#ax.set_ylim([10e-6, 10e0])
ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
# ax.set_ylim([10e-6, 10e0])
plt.show()