Deep Learning-Powered Beamforming for 5G Massive MIMO Systems

• Autorzy: Bendjillali Ridha Ilyas , Bendelhoum Mohammed Sofiane , Tadjeddine Ali Abderrazak , Kamline Miloud

Identyfikatory

DOI

10.26636/jtit.2023.4.1332

• Języki publikacji: EN

Abstrakty

EN

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multipleoutput (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.

Słowa kluczowe

EN

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

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2023
• Tom: nr 4
• Strony: 38--45
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr.

Twórcy

autor

Bendjillali Ridha Ilyas

• Laboratory of Electronic Systems, Telecommunications and Renewable Energies, Department of Technology University Center Nour Bachir, El Bayadh, Algeria

• https://orcid.org/0000-0003-2465-8192

autor

Bendelhoum Mohammed Sofiane

• Laboratory of Electronic Systems, Telecommunications and Renewable Energies, Department of Technology University Center Nour Bachir, El Bayadh, Algeria

• https://orcid.org//0000-0002-9789-8712

autor

Tadjeddine Ali Abderrazak

• Laboratory of Electronic Systems, Telecommunications and Renewable Energies, Department of Technology University Center Nour Bachir, El Bayadh, Algeria

• https://orcid.org/0000-0003-0926-3440

autor

Kamline Miloud

• TIT Laboratory, Department of Electrical Engineering Tahri Mohammed University, Bechar, Algeria

• https://orcid.org/0009-0007-2949-4859

Bibliografia

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