Low-complexity MIMO detection based on belief propagation over pairwise graphs

Seokhyun Yoon, Chan Byoung Chae

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

This paper considers a belief propagation algorithm over pairwise graphical models to develop low-complexity iterative multiple-input multiple-output (MIMO) detectors. The pairwise graphical model is a bipartite graph where a pair of variable nodes are related by an observation node represented by the bivariate Gaussian function obtained by marginalizing the posterior joint probability density under the Gaussian input assumption. Specifically, we consider two types of pairwise models: the fully connected and ring-type. The pairwise graphs are sparse, compared with the conventional graphical model introduced by Bickson et al., insofar as the number of edges connected to an observation node (edge degree) is only two. Consequently, the computations are much easier than those of maximum likelihood (ML) detection, which are similar to the belief propagation (BP) that is run over the fully connected bipartite graph. The link level performance for non-Gaussian input is evaluated via simulations, and the results show the validity of the proposed algorithms. We also customize the algorithm with Gaussian input assumption to obtain the Gaussian BP run over the two pairwise graphical models, and for the ring-type, we prove its convergence to the linear minimum mean square error (MMSE) estimates. Since the maximum a posterior (MAP) estimator for Gaussian input is equivalent to the linear MMSE estimator, it shows the optimality of the scheme for Gaussian input.

Original languageEnglish
Article number6665044
Pages (from-to)2363-2377
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Volume63
Issue number5
DOIs
Publication statusPublished - 2014 Jun

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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