52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import numpy
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import numpy as np
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import itertools
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# TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n
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class SoftDecisionDecoder:
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"""This class naively implements a soft decision decoder. The decoder calculates
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the correlation between the received signal and each codeword and then chooses the
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one with the largest correlation.
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"""
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def __init__(self, G: np.array, H: np.array):
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"""Construct a new SoftDecisionDecoder object.
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:param G: Generator matrix
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:param H: Parity check matrix
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"""
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self._G = G
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self._H = H
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self._datawords, self._codewords = self._gen_codewords()
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self._codewords_bpsk = 1 - 2 * self._codewords # The codewords, but mapped to [-1, 1]^n
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def _gen_codewords(self) -> np.array:
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"""Generate a list of all possible codewords.
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:return: Numpy array of the form [[codeword_1], [codeword_2], ...]
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"""
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k, n = self._G.shape
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# Generate a list of all possible data words
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u_lst = [list(i) for i in itertools.product([0, 1], repeat=k)]
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u_lst = np.array(u_lst)
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# Map each data word onto a codeword
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c_lst = np.dot(u_lst, self._G) % 2
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return u_lst, c_lst
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def decode(self, y: np.array) -> np.array:
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"""Decode a received signal.
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This function assumes a BPSK modulated signal.
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:param y: Vector of received values. (y = x + w, where 'x' is element of [-1, 1]^n
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and 'w' is noise)
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:return: Most probably sent dataword (element of [0, 1]^k)
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"""
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correlations = np.dot(self._codewords_bpsk, y)
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return self._codewords[numpy.argmax(correlations)]
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