Affiliation:
1. School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin, Guangxi 537000, China
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.
Funder
Natural Science Foundation of Guangxi Province
Subject
Electrical and Electronic Engineering
Cited by
1 articles.
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1. The Smart Optimization of Scalable Probabilistic Modeling with Gaussian Processes;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29