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

TL;DR

This paper introduces a decision feedback scheme combined with low density parity check codes for compressing two correlated binary sources modeled by a Hidden Markov Model, achieving near-optimal Slepian-Wolf limits.

Contribution

The paper presents a novel decision feedback approach tailored for sources with Hidden Markov Model correlation, improving compression efficiency.

Findings

Achieves compression close to Slepian-Wolf limits.

Effective for sources with Hidden Markov Model correlation.

Demonstrates the scheme's practical viability.

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.