Monte Carlo Methods for Randomized Likelihood Decoding

Abstract

A randomized decoder that generates the message estimate according to the posterior distribution is known to achieve the reliability comparable to that of the maximum a posteriori probability decoder. With a goal of practical implementations of such a randomized decoder, several Monte Carlo techniques, such as rejection sampling, Gibbs sampling, and the Metropolis algorithm, are adapted to the problem of efficient sampling from the posterior distribution. Analytical and experimental results compare the complexity and performance of these Monte Carlo decoders for simple linear codes and the binary symmetric channel.