International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Data-Driven-Based Beam Selection for Hybrid Beamforming in Ultra-Dense Networks

  • Ju, Sang-Lim (Department of radio and communication engineering, Chungbuk National University) ;
  • Kim, Kyung-Seok (Department of information and communication engineering, Chungbuk National University)
  • Received : 2020.03.28
  • Accepted : 2020.04.06
  • Published : 2020.06.30
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Abstract

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

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