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.
Publisher
National Institute of Telecommunications
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Reference24 articles.
1. S. Belhadj, A.M. Lakhdar, and R.I. Bendjillali, "Performance Comparison of Channel Coding Schemes for 5G Massive Machine Type Communications", The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 22, no. 2, pp. 902-908, 2021.
2. E. Ali, M. Ismail, R. Nordin, and N.F. Abdulah, "Beamforming Techniques for Massive MIMO Systems in 5G: Overview, Classification, and Trends for Future Research", Frontiers of Information Technology & Electronic Engineering, vol. 18, no. 6, pp. 753-772, 2017.
3. R.I. Bendjillali, M. Beladgham, K. Merit, and A.T. Ahmed, "Illumination-Robust Face Recognition Based on Deep Convolutional Neural Networks Architectures", The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 18, no. 2, pp. 1015-1027, 2020.
4. R.I. Bendjillali, M. Beladgham, K. Merit, and M. Kamline, "Enhanced Face Recognition System Based on Deep CNN", 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), Mostaganem, Algeria, 2019.
5. R.I. Bendjillali, M. Beladgham, K. Merit, and A.T. Ahmed, "Improved Facial Expression Recognition Based on DWT Feature for Deep CNN", Electronics, vol. 8, no. 3, art. no. 324, 2019.
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