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A novel MCMC algorithm for near-optimal detection in large-scale uplink mulituser MIMO systems

Datta, Tanumay and Kumar, Ashok N and Chockalingam, A and Rajan, Sundar B (2012) A novel MCMC algorithm for near-optimal detection in large-scale uplink mulituser MIMO systems. In: 2012 Information Theory and Applications Workshop (ITA), 5-10 Feb. 2012, San Diego, CA.

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Official URL: http://dx.doi.org/10.1109/ITA.2012.6181816


In this paper, we propose a low-complexity algorithm based on Markov chain Monte Carlo (MCMC) technique for signal detection on the uplink in large scale multiuser multiple input multiple output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and similar number of uplink users. The algorithm employs a randomized sampling method (which makes a probabilistic choice between Gibbs sampling and random sampling in each iteration) for detection. The proposed algorithm alleviates the stalling problem encountered at high SNRs in conventional MCMC algorithm and achieves near-optimal performance in large systems with M-QAM. A novel ingredient in the algorithm that is responsible for achieving near-optimal performance at low complexities is the joint use of a randomized MCMC (R-MCMC) strategy coupled with a multiple restart strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for large number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users).

Item Type: Conference Paper
Publisher: IEEE
Additional Information: Copyright of this article belongs to IEEE.
Keywords: Large-Scale Multiuser MIMO; Markov Chain Monte Carlo Technique; Gibbs sampling; Detection; Stalling Problem; Randomized Sampling; Multiple Restarts
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 27 Jan 2014 07:14
Last Modified: 27 Jan 2014 07:14
URI: http://eprints.iisc.ac.in/id/eprint/48289

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