An Efficient Joint Source-Channel Decoder with Dynamical Block Priors

TL;DR

This paper introduces an efficient joint source-channel decoder utilizing dynamical block priors derived from statistical mechanics or Markov models, improving decoding efficiency without extensive matrix computations.

Contribution

It presents a novel joint source-channel decoding method that employs dynamical block priors from statistical or Markovian approaches, reducing computational complexity.

Findings

Markovian decoder avoids large matrix diagonalization

Efficient decoding with parametric estimation without side information

Autocorrelation sets form a convex volume, optimized via Simplex algorithm

Abstract

An efficient joint source-channel (s/c) decoder based on the side information of the source and on the MN-Gallager algorithm over Galois fields is presented. The dynamical block priors (DBP) are derived either from a statistical mechanical approach via calculation of the entropy for the correlated sequences, or from the Markovian transition matrix. The Markovian joint s/c decoder has many advantages over the statistical mechanical approach. In particular, there is no need for the construction and the diagonalization of a qXq matrix and for a solution to saddle point equations in q dimensions. Using parametric estimation, an efficient joint s/c decoder with the lack of side information is discussed. Besides the variant joint s/c decoders presented, we also show that the available sets of autocorrelations consist of a convex volume, and its structure can be found using the Simplex algorithm.