Deep Learning-Powered Beamforming for 5G Massive MIMO Systems

Authors

DOI:

https://doi.org/10.26636/jtit.2023.4.1332

Keywords:

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

Abstract

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.

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Published

2023-10-31

Issue

No. 4 (2023)

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Copyright (c) 2023 Ridha Ilyas Bendjillali, Mohammed Sofiane Bendelhoum , Ali Abderrazak Tadjeddine , Miloud Kamline

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. I. Bendjillali, M. S. Bendelhoum, A. A. Tadjeddine, and M. Kamline, “Deep Learning-Powered Beamforming for 5G Massive MIMO Systems”, JTIT, vol. 94, no. 4, pp. 38–45, Oct. 2023, doi: 10.26636/jtit.2023.4.1332.