Year: 2023
Type: article
Abstract: 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.
Source: Sensors
DOI: 10.3390/s23094359
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,669
Funders: Instituto Nacional de Telecomunicações, Science Foundation Ireland, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Ministério da Ciência, Tecnologia e Ensino Superior, Ministério da Ciência, Tecnologia, Inovações e Comunicações, Fundação para a Ciência e a Tecnologia
Awards: SFI2: slicing future internet infrastructures, G1217348491, G1219079710, G1229509979, G1384204577, G139415119, G1669827218, G1756574887, G1781884919, G1834770157, G1873470832, G1985036554, G2333310086, G2476155133, G2577021876, G2616452922, G2618103455, G2628323575, G2686523273, G26889089, G2716566129, G2966720232, G3016618895, G3022852866, G3386799178, G33895191, G3814563887, G3850789590, G4001387230, G4023674271, Reconfigurable intelligent surface aided communications for 6G and beyond, G4283745296, G4292386165, G4434484124, G4594174575, G4626548911, G4680224365, G4904073550, G4931258392, G497239743, G5094188500, G5375716737, G5394680643, G5583756623, G5687621348, G5704058613, G5712472249, G5885973594, G6229767931, G6296463250, G632132665, G6438247056, G6439845092, PROFISSA: Programmable Future Internet for Secure Software Architectures, G663985128, G6755751248, G6903074819, G7071933790, G7198904539, G7526685054, G7586813259, G7609969306, G7696258939, G7748798550, G8241567066, G8475673899, G8581167010, G8636110625, G8761002944, G967101278, G977926491
OpenAlex ID: W4367315342