Pseudo prior belief propagation for densely connected discrete graphs

Jacob Goldberger*, Amir Leshem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

This paper proposes a new algorithm for the linear least squares problem where the unknown variables are constrained to be in a finite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete bipartite graph. Hence, applying the Belief Propagation (BP) algorithm yields very poor results. The Pseudo Prior Belief Propagation (PPBP) algorithm is a variant of the BP algorithm that can achieve near maximum likelihood (ML) performance with low computational complexity. First, we use the minimum mean square error (MMSE) detection to yield a pseudo prior information on each variable. Next we integrate this information into a loopy Belief Propagation (BP) algorithm as a pseudo prior. We show that, unlike current paradigms, the Belief Propagation (BP) algorithm can be advantageous even for dense graphs with many short loops. The performance of the proposed algorithm is demonstrated on the MIMO detection problem based on simulation results.

Original languageAmerican English
Title of host publicationIEEE Information Theory Workshop 2010, ITW 2010
DOIs
StatePublished - 2010
Externally publishedYes
EventIEEE Information Theory Workshop 2010, ITW 2010 - Cairo, Egypt
Duration: 6 Jan 20108 Jan 2010

Publication series

NameIEEE Information Theory Workshop 2010, ITW 2010

Conference

ConferenceIEEE Information Theory Workshop 2010, ITW 2010
Country/TerritoryEgypt
CityCairo
Period6/01/108/01/10

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