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Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Kumar, Ashok and Chandrasekaran, Suresh and Chockalingam, Ananthanarayanan and Rajan, Sundar Sundar (2011) Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms. In: International Conference on Communications, 5-9 June 2011, Kyoto.

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Official URL: http://dx.doi.org/10.1109/icc.2011.5963229

Abstract

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Item Type: Conference Proceedings
Additional Information: Copyright of this article belongs to IEEE.
Keywords: Markov Chain Monte Carlo; Random Search; Message Passing; Large-MIMO Systems; Near-Optimal Low-Complexity Detection
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 02 Apr 2013 08:51
Last Modified: 02 Apr 2013 08:51
URI: http://eprints.iisc.ac.in/id/eprint/46150

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