Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Davi da Silva Brilhante
* ORCID logo ,
Joanna Manjares
,
Rodrigo Moreira
ORCID logo ,
Lucas de Oliveira Veiga
,
José F. de Rezende
ORCID logo ,
Francisco Müller
ORCID logo ,
Aldebaro Klautau
,
Luciano Leonel
,
Felipe Augusto Pereira de Figueiredo
Version 1 : Received: 15 March 2023 / Approved: 15 March 2023 / Online: 15 March 2023 (10:20:16 CET)

A peer-reviewed article of this Preprint also exists.

Brilhante, D.S.; Manjarres, J.C.; Moreira, R.; de Oliveira Veiga, L.; de Rezende, J.F.; Müller, F.; Klautau, A.; Leonel Mendes, L.; P. de Figueiredo, F.A. A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems. Sensors 2023, 23, 4359. Brilhante, D.S.; Manjarres, J.C.; Moreira, R.; de Oliveira Veiga, L.; de Rezende, J.F.; Müller, F.; Klautau, A.; Leonel Mendes, L.; P. de Figueiredo, F.A. A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems. Sensors 2023, 23, 4359.

Abstract

The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

Keywords

Artificial Intelligence; Beamforming; Machine Learning; MIMO; 5G; 6G

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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