Now calculating the error rate based on the codewrords, not datawords
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@@ -19,27 +19,25 @@ def count_bit_errors(d: np.array, d_hat: np.array) -> int:
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# TODO: Fix uses of n, k, a everywhere
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def test_decoder(encoder: typing.Any,
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def test_decoder(x: np.array,
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decoder: typing.Any,
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d: np.array,
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SNRs: typing.Sequence[float] = np.linspace(1, 4, 7),
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target_bit_errors: int = 100,
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N_max: int = 10000) \
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-> typing.Tuple[np.array, np.array]:
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"""Calculate the Bit Error Rate (BER) for a given decoder for a number of SNRs.
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This function prints its progress to stdout.
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This function assumes the all-zeros assumption holds. Progress is printed to stdout.
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:param encoder: Instance of the encoder used to generate the codeword to transmit
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:param x: Codeword to be sent (Element of [0, 1]^n)
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:param decoder: Instance of the decoder to be tested
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:param d: Dataword (element of [0, 1]^n)
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:param SNRs: List of SNRs for which the BER should be calculated
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:param target_bit_errors: Number of bit errors after which to stop the simulation
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:param N_max: Maximum number of iterations to perform for each SNR
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:return: Tuple of numpy arrays of the form (SNRs, BERs)
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"""
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x = encoder.encode(d)
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x_bpsk = 1 - 2 * x # Map x from [0, 1]^n to [-1, 1]^n
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BERs = []
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for SNR in tqdm(SNRs, desc="Calculating Bit-Error-Rates",
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@@ -55,14 +53,12 @@ def test_decoder(encoder: typing.Any,
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leave=False,
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}"):
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# TODO: Is this a valid simulation? Can we just add AWGN to the codeword,
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# ignoring and modulation and (e.g. matched) filtering?
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y = noise.add_awgn(x, SNR, signal_amp=np.sqrt(2))
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y = noise.add_awgn(x_bpsk, SNR, signal_amp=np.sqrt(2))
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y_hat = decoder.decode(y)
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total_bit_errors += count_bit_errors(d, y_hat)
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total_bits += d.size
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total_bit_errors += count_bit_errors(x, y_hat)
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total_bits += x.size
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if total_bit_errors >= target_bit_errors:
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break
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