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
http://hdl.handle.net/10993/47072
RSSI-Based Hybrid Beamforming Design with Deep Learning
English
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 []
7-Jun-2020
2020 IEEE International Conference on Communications Proceedings
IEEE
Yes
International
978-1-7281-5089-5
Dublin
Ireland
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.
http://hdl.handle.net/10993/47072

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
RSSI-Based Hybrid Beamforming Design with Deep Learning.pdfPublisher postprint285.97 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.