Distributed Parameter Estimation with Side Information: A Factor Graph Approach

“THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD”. In this paper, a low complexity algorithm for distributed maximum likelihood estimation of a binary symmetric source (BSS) using side-information is proposed. The estimation is formulated as an incomplete-data problem and is solved by the expectation-maximization (EM) algorithm. A low-complexity implementation of the algorithm using coset codes and LDPC-based syndrome decoding with message passing over factor-graph is also proposed. The algorithm iS a generalization of the LDPC-based syndrome decoding algorithm for the case when the probability distribution of the source is not known $a$-priori. Hence, the algorithm may be considered as a tool for achieving the corner points of the Slepian-Wolf (SW) region in distributed coding when the correlation channel information is not available. The estimation efficiency is studied by comparing the mean square error with the achievable Fisher Information.