A Low Complexity MIMO Detection Based on Pair-Wise Markov Random Fields

Low complexity, iterative MIMO detection algorithms are derived based on pair-wise Markov random fields (MRF). We consider two types, namely, the fully-connected and the ring type MRF and, for the edge potentials, we use the bivariate Gaussian function obtained by marginalizing the posterior joint probability density under Gaussian-input assumption. Since the corresponding factor graphs has only 2 edges per factor node, the computations are much easier than that of ML which is similar to the belief propagation algorithm run over the fully connected factor graph. Compared to the complexity of ML, O(2^mM), the proposed scheme for the fully-connected and the ring-type MRF is shown to have only O(M ·(M-1) 2^m-1) and O(M·2^m), respectively, for M being the input dimension and m the number of bits per data symbol. The performances are evaluated, via simulation, in terms of bit error rate with DVB-S2 LDPC coding.