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Applied Mathematics & Information Sciences

Author Country (or Countries)

China

Abstract

For a multiple-input multiple-output (MIMO) system with more antennas at the receiver than the transmitter, selecting the same number of receiver antennas as the number of transmitter antennas can takemost of the advantages ofMIMO capacity performance and at the same time reduce the system hardware cost and computational complexity. In this paper, a novel effective low complexity near-optimal antenna selection algorithms based on maximization channel capacity is proposed for the MIMO array configuration. Different from many existing fast antenna selection algorithms which obtain the sub-optimal channel sub matrix by adding or removing one row per step exploit re-computing formula, our algorithm acquires the near-optimal channel matrix by a faster updating formula. In such faster updating formula, the formula is updating rather than re-computing, so that the matrix inverse operation is avoided. Due to using an effective iteration processing, our antenna selection algorithm reduces computational complexity and leads to a substantial improvement in the capacity optimally for moderating to high signal to noise ratio (SNRs), and obtains almost the same capacity and bit error ratio (BER) performance as that of the exhaustive-search-based optimal antenna selection algorithm. Compared to the conventional sub-optimal antenna selection algorithms, our algorithm has lower computational complexity and achieves almost the same capacity and BER performance as the optimal selection algorithm. Finally, theoretical analysis and simulation results illustrate that the new algorithm outperforms the existing sub-optimal antenna selection methods.

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