Reference : RSSI-Based Hybrid Beamforming Design with Deep Learning
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hojatian, Hamed []
Ha, Vu Nguyen mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Nadal, Jérémy []
Frigon, Jean-François []
Leduc-Primeau, François []
2020 IEEE International Conference on Communications Proceedings
ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
from 07-06-2020 to 11-06-2020
[en] deep learning ; Hybrid beamforming ; RSSI
[en] 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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