A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems
Davi da Silva Brilhante,
Joanna Carolina Manjarres,
Rodrigo Moreira,
Lucas de Oliveira Veiga,
José F. de Rezende,
Francisco Müller,
Aldebaro Klautau,
Luciano Leonel Mendes,
Felipe A. P. de Figueiredo
Affiliations
Davi da Silva Brilhante
Laboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, Brazil
Joanna Carolina Manjarres
Laboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, Brazil
Rodrigo Moreira
Institute of Exact and Technological Sciences (IEP), Federal University of Viçosa (UFV), Rio Paranaíba 38810-000, MG, Brazil
Lucas de Oliveira Veiga
Institute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
José F. de Rezende
Laboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, Brazil
Francisco Müller
LASSE-5G and IoT Research Group, Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil
Aldebaro Klautau
LASSE-5G and IoT Research Group, Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil
Luciano Leonel Mendes
National Institute of Telecommunications (INATEL), Santa Rita do Sapucaí 37540-000, MG, Brazil
Felipe A. P. de Figueiredo
National Institute of Telecommunications (INATEL), Santa Rita do Sapucaí 37540-000, MG, Brazil
Modern wireless communication systems rely heavily on multiple antennas and their corresponding signal processing to achieve optimal performance. As 5G and 6G networks emerge, beamforming and beam management become increasingly complex due to factors such as user mobility, a higher number of antennas, and the adoption of elevated frequencies. Artificial intelligence, specifically machine learning, offers a valuable solution to mitigate this complexity and minimize the overhead associated with beam management and selection, all while maintaining system performance. Despite growing interest in AI-assisted beamforming, beam management, and selection, a comprehensive collection of datasets and benchmarks remains scarce. Furthermore, identifying the most-suitable algorithm for a given scenario remains an open question. This article aimed to provide an exhaustive survey of the subject, highlighting unresolved issues and potential directions for future developments. The discussion encompasses the architectural and signal processing aspects of contemporary beamforming, beam management, and selection. In addition, the article examines various communication challenges and their respective solutions, considering approaches such as centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.
