Deep Learning-Powered Beamforming for 5G Massive MIMO Systems

Autor:	Ridha Ilyas Bendjillali, Mohammed Sofiane Bendelhoum, Ali Abderrazak Tadjeddine, Miloud Kamline
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	5G digital beamforming hybrid beamforming massive MIMO ResNeSt Telecommunication TK5101-6720 Information technology T58.5-58.64
Zdroj:	Journal of Telecommunications and Information Technology, Vol 4, Iss 4 (2023)
Druh dokumentu:	article
ISSN:	1509-4553 1899-8852
DOI:	10.26636/jtit.2023.4.1332
Popis:	In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/73f1d52ee5be4840855b8d4a7bc0ee8b Zobrazit plný text záznamu View record in DOAJ