List sequence MAP decoding

A list sequence (LS) maximum a posteriori probability (MAP) decoding algorithm for convolutional codes, that takes into account bitwise a priori probabilities and produces a rank ordered list of L sequence MAP estimates, can be obtained by modification of the metric increments of the serial list Viterbi algorithm. In this paper, we study the performance of LS-MAP decoding with genie-assisted error detection on the additive white Gaussian noise channel. Computer simulations and approximate analytical expressions, based on geometrical considerations are presented. We focus on the frame error rate and it is concluded that LS-MAP decoding with L > 1 often exploits a priori information more efficiently than conventional single sequence MAP decoding (L = 1). This leads in many cases to larger relative gains with LS-MAP than LS-ML decoding as L increases.