On Decoding Binary Quasi-Perfect Codes Over Markov Noise Channels

We study the decoding problem when a binary linear quasi-perfect code is transmitted over a binary channel with additive Markov noise. After examining the properties of the channel block transition distribution, we show a near equivalence relationship between strict maximum likelihood and strict minimum Hamming distance decoding for a range of channel parameters and the code´s minimum distance. As a result, an improved minimum distance decoder is proposed and simulations illustrating its benefits are provided.