A low-complexity receive-antenna-selection algorithm for MIMO-OFDM wireless systems
Name
Liu-2009-A Low-Complexity Receive-Antenna-Selection Algorithm for MIMO-OFDM Wireless Systems.pdf
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Author(s)
Malik, Wasim Qamar
Liu, Yi
Zhang, Yangyang
Ji, Chunlin
Edwards, David J.
Date Issued
June 2009
Journal
IEEE Transactions on Vehicular Technology
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yi Liu et al. “A Low-Complexity Receive-Antenna-Selection Algorithm for MIMO–OFDM Wireless Systems.” Vehicular Technology, IEEE Transactions on 58.6 (2009): 2793-2802. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
In this paper, a novel low-complexity antenna-selection algorithm based on a constrained adaptive Markov chain Monte Carlo (CAMCMC) optimization method is proposed to approach the maximum capacity or minimum bit error rate (BER) of receive-antenna-selection multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. We analyze the performance of the proposed system as the control parameters are varied and show that both the channel capacity and the system BER achieved by the proposed CAMCMC selection algorithm are close to the optimal results obtained by the exhaustive search (ES) method. We further demonstrate that this performance can be achieved with less than 1% of the computational complexity of the ES rule and is independent of the antenna-selection criteria, outage rate requirements, antenna array configuration, and channel frequency selectivity. Similar to the existing antenna-selection algorithms, both channel capacity and system BER improvements achieved by the proposed CAMCMC method are reduced as the channel frequency selectivity increases. Therefore, we conclude that, whether it is designed to maximize the channel capacity or minimize the system BER, the CAMCMC-optimization-method-based antenna-selection technique is appropriate for a MIMO-OFDM system with low frequency selectivity.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/TVT.2008.2010943
