Fixed [0, 1] and [-1, 1] issue; comments; stopping simulation after n bit errors
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@ -68,25 +68,26 @@ class ProximalDecoder:
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Detailed in 3.2, p. 3 (Equation (15)).
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:param x:
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:return:
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:return: x clipped to [-eta, eta]^n
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"""
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return np.clip(x, -self._eta, self._eta)
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def _check_parity(self, y_hat: np.array) -> bool:
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"""Perform a parity check for a given codeword.
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:param y_hat: codeword to be checked
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:param y_hat: codeword to be checked (element of [-1, 1]^n)
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:return: True if the parity check passes, i.e. the codeword is valid. False otherwise
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"""
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y_hat_binary = (y_hat == 1) * 1 # Map the codeword from [-1, 1]^n to [0, 1]^n
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syndrome = np.dot(self._H, y_hat) % 2
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return not np.any(syndrome)
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def decode(self, y: np.array) -> np.array:
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"""Decode a received signal. The algorithm is detailed in 3.2, p.3.
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This function assumes an AWGN channel.
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This function assumes a BPSK-like modulated signal ([-1, 1]^n instead of [0, 1]^n) and an AWGN channel.
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:param y: Vector of received values
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:param y: Vector of received values. (y = x + n, where 'x' is element of [-1, 1]^m and 'n' is noise)
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:return: Most probably sent symbol
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"""
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s = 0
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@ -45,18 +45,21 @@ def count_bit_errors(d: np.array, d_hat: np.array) -> int:
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def test_decoder(decoder: typing.Any,
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c: np.array,
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SNRs: typing.Sequence[float] = np.linspace(1, 4, 7),
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N=10000) \
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target_bit_errors=100,
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N_max=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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:param decoder: Instance of the decoder to be tested
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:param c: Codeword whose transmission is to be simulated
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:param c: Codeword whose transmission is to be simulated (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 N: Number of iterations to perform for each SNR
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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 = c * 2 - 1 # Map the codeword 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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position=0,
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@ -64,19 +67,22 @@ def test_decoder(decoder: typing.Any,
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total_bit_errors = 0
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for n in tqdm(range(N), desc=f"Simulating for SNR = {SNR} dB",
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for n in tqdm(range(N_max), desc=f"Simulating for SNR = {SNR} dB",
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position=1,
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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, ignoring and modulation and (
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# e.g. matched) filtering?
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y = add_awgn(c, SNR)
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y = add_awgn(x, SNR)
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y_hat = decoder.decode(y)
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total_bit_errors += count_bit_errors(c, y_hat)
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total_bits = c.size * N
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if total_bit_errors >= target_bit_errors:
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break
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total_bits = c.size * N_max
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BERs.append(total_bit_errors / total_bits)
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return np.array(SNRs), np.array(BERs)
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@ -27,12 +27,13 @@ def main():
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print(f"Simulating with c = {c}")
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decoder = proximal.ProximalDecoder(H, K=100, gamma=0.01)
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SNRs, BERs = utility.test_decoder(decoder, c, SNRs=[1, 3, 20], N=1000)
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SNRs, BERs = utility.test_decoder(decoder, c, SNRs=[1, 3, 5, 7], N_max=10000)
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data = pd.DataFrame({"SNR": SNRs, "BER": BERs})
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ax = sns.lineplot(data=data, x="SNR", y="BER")
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ax.set_ylim([0, 1])
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ax.set(yscale="log")
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#ax.set_ylim([10e-6, 10e0])
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plt.show()
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@ -0,0 +1 @@
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"""This package contains unit tests."""
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@ -20,6 +20,7 @@ class CountBitErrorsTestCase(unittest.TestCase):
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self.assertEqual(utility.count_bit_errors(d3, y_hat3), 4)
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# TODO: Is this correct?
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class NoiseAmpFromSNRTestCase(unittest.TestCase):
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"""Test case for noise amplitude calculation."""
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def test_get_noise_amp_from_SNR(self):
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