Title:

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

Group publication title:

2023, nr 4, JTIT-artykuły

Creator:

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

Subject and Keywords:

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.

Number:

4

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Date:

2023

Resource Type:

artykuł

DOI:

doi.org/10.26636/jtit.2023.4.1332

ISSN:

1509-4553

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

License:

CC BY 4.0

rights owner:

Instytut Łączności - Państwowy Instytut Badawczy