Models, Statistics, and Rates of Binary Correlated Sources

TL;DR

This paper analyzes models of binary correlated sources, providing statistical characterizations, joint entropy expressions, and demonstrating the convergence of entropy rates for large source numbers, which informs distributed communication performance limits.

Contribution

It introduces and compares various models of binary correlated sources, deriving joint entropy formulas and showing entropy rate convergence, extending existing theoretical results.

Findings

Joint entropy expressions for different models

Asymptotic entropy rate converges for large number of sources

Generalizes previous results on information-theoretic limits

Abstract

This paper discusses and analyzes various models of binary correlated sources, which may be relevant in several distributed communication scenarios. These models are statistically characterized in terms of joint Probability Mass Function (PMF) and covariance. Closed-form expressions for the joint entropy of the sources are also presented. The asymptotic entropy rate for very large number of sources is shown to converge to a common limit for all the considered models. This fact generalizes recent results on the information-theoretic performance limit of communication schemes which exploit the correlation among sources at the receiver.