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Optimal beamforming-selection spatial precoding using population-based stochastic optimization for massive wireless MIMO communication systems
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DOI：10.1016/j.jfranklin.2017.03.002.png)
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The beamforming-based spatial precoding (BBSP) method has been proposed to reduce the overheads of the downlink training and the channel state information feedback in the frequency-division duplex (FDD) massive multiple-input-multiple-output (MIMO) wireless communication systems. However, the original BBSP method suffers from the interference problem at user equipments (UEs) because of using a set of pre-defined fixed beamforming coefficients. Moreover, the BBSP method can not deal with the performance degradation due to mutual coupling (MC) effect because of massive antennas deployed at transmitter and receiver. This paper presents a precoding method that incorporates a beamformingselection spatial preceding (BSSP) scheme with a population-based stochastic optimization algorithm such that the designed beamforming coefficients can greatly reduce the severe interference between UEs and alleviate the MC effect on the performance of massive MIMO systems. The proposed method can not only achieve better bit error rate (BER) performance than the conventional BBSP method, but also preserves the advantages of the BBSP method having lower overheads of the downlink training and the CSI feedback. In particular, we propose an appropriate fitness function based on an averaged BER formula for the population-based stochastic optimization algorithm to find the optimal beamforming coefficients. Numerical simulations are also presented for both the urban-macro and the urban-micro wireless MIMO scenarios to validate the superior BER performance of the proposed precoding method as compared to the existing BBSP method. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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