Pseudo Prior Belief Propagation for densely connected discrete graphs

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


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 publicationInformation Theory (ITW 2010, Cairo), 2010 IEEE Information Theory Workshop on
StatePublished - 2010

Bibliographical note

Place of conference:Cairo


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