Abstract
With the growing of base station antenna number in massive multiple-input-multiple-output (MIMO), the complexity and the cost of the system rise rapidly. Fortunately, the antenna selection is an effective way to solve the problem, which can capture most of the advantages of massive MIMO. In this paper, a novel global-searching-based iterative swapping antenna selection method is proposed under the partial channel knowledge case for massive MIMO based on capacity maximization. The method is investigated with detailed mathematical derivations, as well as convergence discussion. Both global-searching-based “local” swapping (GSL-swapping) and global-searching-based “global” swapping algorithms are proposed for massive MIMO with imperfect channel state information (CSI). The former exchanges the selected antenna with the unselected one which is “better” than the selected one. Different from the GSL-swapping algorithm, the latter only swaps the “worst” selected antenna with the “best” unselected antenna which is “better” than the “worst” selected one. In addition, an improved global-searching-based “global” swapping algorithm with imperfect CSI is proposed to further decrease the complexity. Due to the “global searching” characteristic, the proposed algorithms can obtain a near optimal performance. Numerical simulations are provided to validate the proposed algorithms.
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