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Harnessing Intelligent RIS for Optimized Capacity and Latency in 6G Cooperative NOMA Systems: A Phase Shift Control Approach
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Author(s): Mohamed Hassan (Department of Electrical Engineering, Omdurman Islamic University, Omdurman, Sudan), Khalid Hamid (Department of Electrical Engineering, Omdurman Islamic University, Omdurman, Sudan), Hashim Elshafie (Department of Engineering, College of Computer Science, King Khalid University, Saudi Arabia), Elmuntaser Hassan (Department of Computer Science, Al-Neelain University, Khartoum, Sudan), Rashid A. Saeed (Electronics Engineering Schools, Sudan University of Science and Technology, Khartoum, Sudan), Hesham Alhumyani (Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia)and Abdullah Alenizi (Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi Arabia)
Copyright: 2024
Volume: 16
Issue: 1
Pages: 23
Source title:
International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.366587
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Abstract
This article examines the performance of cooperative NOMA systems in a massive MIMO configuration inside the sixth-generation (6G) network, including scenarios with and without reconfigurable intelligent surfaces (RIS). The emphasis is on comprehending the effects of implementing static, dynamic, and intelligent RIS on the capacity of the cooperative NOMA system with differing user quantities. The study examines how different user loads and RIS densities affect average latency and effective area spectral efficiency (EASE). Integration of a proposal system in a novel way with water-filling in the multi-user channel logarithm to enhance capacity, average latency, and EASE for diverse user densities. The results show that RIS significantly improves capacity, average latency, and EASE, especially with intelligent RIS. All scenarios, in conjunction with the suggested algorithm, markedly improve network performance, particularly under conditions of increased user demand. Additionally, it enhances the system's capability and spectrum efficiency, especially when deployment numbers rise.
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