Length-Constrained MAP Decoding of Variable-Length Encoded Markov Sequences

Zhe Wang, Xiaolin Wu

Abstract: In this paper, we consider the problem of length-constrained maximum a posteriori decoding of a Markov sequence that is variable-length encoded and transmitted over a binary symmetric channel. We convert this problem into one of a maximum-weight k-link path in a weighted directed acyclic graph. The induced graph-optimization problem can be solved by a fast parameterized search algorithm that finds either the optimal solution with high probability, or a good approximate solution otherwise. The proposed algorithm has lower complexity and superior performance than the previous approximation algorithms