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.
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