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http://dx.doi.org/10.6109/jkiice.2021.25.11.1559

Deep Reinforcement Learning based Antenna Selection Scheme For Reducing Complexity and Feedback Overhead of Massive Antenna Systems  

Kim, Ryun-Woo (Department of Information and Communication Engineering, Gyeongsang National University)
Jeong, Moo-Woong (Smart Ship ICT-Convergence Research Center, Research Institute of Medium & Small Shipbuilding)
Ban, Tae-Won (Department of Information and Communication Engineering, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.11, 2021 , pp. 1559-1565 More about this Journal
Abstract
In this paper, an antenna selection scheme is proposed in massive multi-user multiple input multiple output (MU-MIMO) systems. The proposed antenna selection scheme can achieve almost the same performance as a conventional scheme while significantly reducing the overhead of feedback by using deep reinforcement learning (DRL). Each user compares the channel gains of massive antennas in base station (BS) to the L-largest channel gain, converts them to one-bit binary numbers, and feed them back to BS. Thus, the feedback overhead can be significantly reduced. In the proposed scheme, DRL is adopted to prevent the performance loss that might be caused by the reduced feedback information. We carried out extensive Monte-Carlo simulations to analyze the performance of the proposed scheme and it was shown that the proposed scheme can achieve almost the same average sum-rates as a conventional scheme that is almost optimal.
Keywords
MIMO; Massive MIMO networks; Antenna selection; Reinforcement learning; BinaryFeedback;
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