Abstract
Antenna selection techniques are extensively applied to reduce hardware cost and power consumption in multiple-input multiple-output (MIMO) systems. This paper proposed a low-cost antenna selection method for system sum-rate maximization based on multiclass scalable Gaussian process classification (SGPC) which is capable to perform analytical inference and is scalable for massive data. Simulation results show that the average sum-rate obtained by SGPC is 1. 9 bps/Hz more than that obtained by conventional optimization driven user-centric antenna selection (UCAS) algorithm and 1 bps/Hz more than that obtained by the up-to-date learning scheme based on a deep neural network (DNN) when signal-to-noise ratio (SNR) is 10 dB, the number of total antennas at BS is 6, the number of selected antennas is 4, and the number of single-antenna users is 4. The superiority of SGPC over UCAS and DNN is more obvious as SNR, the number of selected antennas, or the number of users increases.
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