Implementation of a Markov chain Monte Carlo based multiuser/MIMO detector
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Communications
Approaching MIMO capacity using bitwise Markov chain Monte Carlo detection
IEEE Transactions on Communications
Near-optimal detection in MIMO systems using Gibbs sampling
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Near-capacity iteratively decoded Markov-Chain Monte-Carlo aided BLAST system
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A Gibbs sampling based MAP detection algorithm for OFDM over rapidly varying mobile radio channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Low complexity Markov chain Monte Carlo detector for channels with intersymbol interference
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Markov chain Monte Carlo detection methods for high SNR regimes
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Markov chain minimum bit error rate detection for multi-functional MIMO uplink
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Markov chain monte carlo detectors for channels with intersymbol interference
IEEE Transactions on Signal Processing
Low complexity MLSE equalization in highly dispersive Rayleigh fading channels
EURASIP Journal on Advances in Signal Processing - Special issue on advanced equalization techniques for wireless communications
A parallel VLSI architecture for Markov chain Monte Carlo based MIMO detection
Proceedings of the 23rd ACM international conference on Great lakes symposium on VLSI
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In this paper, we develop novel Bayesian detection methods that are applicable to both synchronous code-division multiple-access and multiple-input multiple-output communication systems. Markov chain Monte Carlo (MCMC) simulation techniques are used to obtain Bayesian estimates (soft information) of the transmitted symbols. Unlike previous reports that widely use statistical inference to estimate a posteriori probability (APP) values, we present alternative statistical methods that are developed by viewing the underlying problem as a multidimensional Monte Carlo integration. We show that this approach leads to results that are similar to those that would be obtained through a proper Rao-Blackwellization technique and thus conclude that our proposed methods are superior to those reported in the literature. We also note that when the channel signal-to-noise ratio is high, MCMC simulator experiences some very slow modes of convergence. Thus accurate estimation of APP values requires simulations of very long Markov chains, which may be infeasible in practice. We propose two solutions to this problem using the theory of importance sampling. Extensive computer simulations show that both solutions improve the system performance greatly. We also compare the proposed MCMC detection algorithms with the sphere decoding and minimum mean square error turbo detectors and show that the MCMC detectors have superior performance.