- Tytuł:
- Deep Learning-Powered Beamforming for 5G Massive MIMO Systems
- Autorzy:
-
Bendjillali, Ridha Ilyas
Bendelhoum, Mohammed Sofiane
Tadjeddine, Ali Abderrazak
Kamline, Miloud - Powiązania:
- https://bibliotekanauki.pl/articles/27312956.pdf
- Data publikacji:
- 2023
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Tematy:
-
5G
digital beamforming
hybrid beamforming
massive MIMO
ResNeSt - Opis:
- 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.
- Źródło:
-
Journal of Telecommunications and Information Technology; 2023, 4; 38--45
1509-4553
1899-8852 - Pojawia się w:
- Journal of Telecommunications and Information Technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł