RSSI-Based Hybrid Beamforming Design with Deep Learning

• URL: http://arxiv.org/abs/2003.06042v1
• Date: Thu, 12 Mar 2020 22:22:49 GMT
• Title: RSSI-Based Hybrid Beamforming Design with Deep Learning
• Authors: Hamed Hojatian, Vu Nguyen Ha, J\'er\'emy Nadal, Jean-Fran\c{c}ois Frigon, and Fran\c{c}ois Leduc-Primeau
• Abstract summary: Hybrid beamforming is a promising technology for 5G millimetre-wave communications. implementation is challenging in practical multiple-input multiple-output systems. Deep learning method is proposed to perform associated optimization with reasonable learning.
• Score: 4.037009782513272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

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