A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems
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 antenna...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4359 |
| _version_ | 1797601692041084928 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Davi da Silva Brilhante |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:07:11Z |
| format | Article |
| id | doaj.art-c70d402818e047b4a75263a8b1b16bc3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:07:11Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c70d402818e047b4a75263a8b1b16bc32023-11-17T23:43:19ZengMDPI AGSensors1424-82202023-04-01239435910.3390/s23094359A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G SystemsDavi da Silva Brilhante0Joanna Carolina Manjarres1Rodrigo Moreira2Lucas de Oliveira Veiga3José F. de Rezende4Francisco Müller5Aldebaro Klautau6Luciano Leonel Mendes7Felipe A. P. de Figueiredo8Laboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, BrazilLaboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, BrazilInstitute of Exact and Technological Sciences (IEP), Federal University of Viçosa (UFV), Rio Paranaíba 38810-000, MG, BrazilInstitute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá 37500-903, MG, BrazilLaboratory for Modeling, Analysis, and Development of Networks and Computer Systems (LAND), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, BrazilLASSE-5G and IoT Research Group, Federal University of Pará (UFPA), Belém 66075-110, PA, BrazilLASSE-5G and IoT Research Group, Federal University of Pará (UFPA), Belém 66075-110, PA, BrazilNational Institute of Telecommunications (INATEL), Santa Rita do Sapucaí 37540-000, MG, BrazilNational Institute of Telecommunications (INATEL), Santa Rita do Sapucaí 37540-000, MG, BrazilModern 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.https://www.mdpi.com/1424-8220/23/9/4359artificial intelligencebeamformingmachine learningMIMO5G6G |
| spellingShingle | 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 A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems Sensors artificial intelligence beamforming machine learning MIMO 5G 6G |
| title | A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems |
| title_full | A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems |
| title_fullStr | A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems |
| title_full_unstemmed | A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems |
| title_short | A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems |
| title_sort | literature survey on ai aided beamforming and beam management for 5g and 6g systems |
| topic | artificial intelligence beamforming machine learning MIMO 5G 6G |
| url | https://www.mdpi.com/1424-8220/23/9/4359 |
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