Markov chain Monte Carlo detection methods for high SNR regimes

Authors:
Salam Akoum;Ronghui Peng;Rong-Rong Chen;Behrouz Farhang-Boroujeny
Affiliations:
Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah;Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah;Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah;Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

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Abstract

Statistical detectors that are based on Markov chain Monte Carlo (MCMC) simulators have emerged as promising low-complexity solutions to both multiple-input multiple-output (MIMO) and code division multiple access (CDMA) communication systems. While these types of detectors achieve unprecedented near capacity performance, i.e., when operated in low signal-to-noise ratio (SNR) regime, they exhibit a serious problem at medium to high SNR regimes, referred to as the "stalling" problem. In this paper, we investigate the sources of this degradation and propose a new search strategy called constrained MCMC to remedy the issue of stalling.