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Abstract: Pair-wise Markov random fields MRF are considered for application to thedevelopment of low complexity, iterative MIMO detection. Specifically, weconsider two types of MRF, namely, the fully-connected and ring-type. For theedge potentials, we use the bivariate Gaussian function obtained bymarginalizing the posterior joint probability density under the Gaussianassumption. Since the corresponding factor graphs are sparse, in the sense thatthe number of edges connected to a factor node edge degree is only 2, thecomputations are much easier than that of ML, which is similar to the beliefpropagation BP, or sum-product, algorithm that is run over the fullyconnected factor graph. The BER performances for non-Gaussian input areevaluated via simulation, and the results show the validity of the proposedalgorithms. We also customize the algorithm for Gaussian input to obtain theGaussian BP that is run over the two MRF and proves its convergence in mean tothe linear MMSE estimates. The result lies on the same line of those in 16and 24, but with differences in its graphical model and the message passingrule. Since the MAP estimator for the Gaussian input is equivalent to thelinear MMSE estimator, it shows the optimality, in mean, of the scheme forGaussian input.



Autor: Seokhyun Yoon, Jun Heo

Fuente: https://arxiv.org/







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