Decision Feedback Based Scheme for Slepian-Wolf Coding of sources with Hidden Markov Correlation

TL;DR

This paper introduces a decision feedback scheme for compressing two binary sources with correlation modeled by a Hidden Markov Model, achieving near Slepian-Wolf limits using LDPC codes.

Contribution

The paper presents a novel decision feedback approach for Slepian-Wolf coding of sources with HMM-based correlation, improving compression efficiency.

Findings

Achieves compression close to Slepian-Wolf limits

Utilizes LDPC codes with decision feedback

Effective for sources with Hidden Markov Model correlation

Abstract

We consider the problem of compression of two memoryless binary sources, the correlation between which is defined by a Hidden Markov Model (HMM). We propose a Decision Feedback (DF) based scheme which when used with low density parity check codes results in compression close to the Slepian Wolf limits.