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In this paper, relaxed belief propagation (RBP) based detectors are proposedfor multiple-input multiple-out (MIMO) system. The factor graph is leveraged torepresent the MIMO channels, and based on which our algorithms are developed.Unlike the existing complicated standard belief propagation (SBP) detector thatconsiders all the edges of the factor graph when updating messages, theproposed RBP focuses on partial edges, which largely reduces computationalcomplexity. In particular, relax degree is introduced in to determine how manyedges to be selected, whereby RBP is a generalized edge selection based BPmethod and SBP is a special case of RBP having the smallest relax degree.Moreover, we propose a novel Gaussian approximation with feedback informationmechanism to enable the proposed RBP detector. In order to further improve thedetection performance, we also propose to cascade a minimum mean square error(MMSE) detector before the RBP detector, from which pseudo priori informationis judiciously exploited. Convergence and complexity analyses, along with thenumerical simulation results, verify that the proposed RBP outperform other BPmethods having the similar complexity, and the MMSE cascaded RBP evenoutperform SBP at the largest relax degree in large MIMO systems.

Relaxed Belief Propagation for MIMO Detection