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RSSI-Based Hybrid Beamforming Design with Deep Learning
Hojatian, Hamed; HA, Vu Nguyen; Nadal, Jérémy et al.
2020In 2020 IEEE International Conference on Communications Proceedings
Peer reviewed
 

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Keywords :
deep learning; Hybrid beamforming; RSSI
Abstract :
[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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Hojatian, Hamed
HA, Vu Nguyen  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Nadal, Jérémy
Frigon, Jean-François
Leduc-Primeau, François
External co-authors :
yes
Language :
English
Title :
RSSI-Based Hybrid Beamforming Design with Deep Learning
Publication date :
07 June 2020
Event name :
ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Event date :
from 07-06-2020 to 11-06-2020
Audience :
International
Main work title :
2020 IEEE International Conference on Communications Proceedings
Publisher :
IEEE, Dublin, Ireland
ISBN/EAN :
978-1-7281-5089-5
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 11 May 2021

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