Tytuł:

Deep Learning-Powered Beamforming for 5G Massive MIMO Systems, Journal of Telecommunications and Information Technology, 2023, nr 4

Tytuł publikacji grupowej:

2023, nr 4, JTIT-artykuły

Autor:

Bendjillali, Ridha Ilyas ; Bendelhoum, Mohammed Sofiane ; Tadjeddine, Ali Abderrazak ; Kamline, Miloud

Temat i słowa kluczowe:

5G ; digital beamforming ; hybrid beamforming ; massive MIMO ; ResNeSt

Abstrakt:

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.

Numer:

4

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Data wydania:

2023

Typ zasobu:

artykuł

DOI:

doi.org/10.26636/jtit.2023.4.1332

ISSN:

1509-4553

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Licencja:

CC BY 4.0

Właściciel praw:

Instytut Łączności - Państwowy Instytut Badawczy