Soft-decision syndrome-based decoder for convolutional codes

Abstract

A soft-decision decoder computes a maximum likelihood estimated transmitted code from a demodulated received signal with less complexity compared to a conventional Viterbi soft-decision decoder. A symbol-by-symbol detector determines a hard-decision vector from a demodulated received signal vector. A syndrome calculator multiplies the hard-decision vector by a scalar parity check matrix to calculate a syndrome vector. A syndrome modifier examines the syndrome vector for predetermined syndrome patterns corresponding to isolated errors in the hard-decision vector. Syndrome patterns are removed from the syndrome vector to create a modified syndrome vector, and the corresponding error in the hard-decision vector is recorded in an estimated error vector. A syndrome-based decoder uses the modified syndrome vector to create a simplified error trellis to produce a remaining error vector. Subtracting the estimated error vector and the remaining error vector from the hard-decision vector creates a maximum likelihood estimated transmitted code vector.