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Large-scale MIMO detection using MCMC approach with blockwise sampling

journal contribution
posted on 2016-09-01, 00:00 authored by L Bai, T Li, J Liu, Q Yu, Jinho ChoiJinho Choi
In this paper, a low-complexity approach for the large-scale (underdetermined) multiple-input multiple-output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein's algorithm is employed in each sub-system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD). From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC-based approach under low-correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near-optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR-based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.

History

Journal

IEEE transactions on communications

Volume

64

Issue

9

Pagination

3697 - 3707

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

0090-6778

Language

eng

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2016, IEEE