Dynamic RIS-Enabled Massive MIMO NOMA Systems Power Allocation Optimization for 6G Using Machin Learning Approach

Authors

Mohamed Hassan, Department of Electrical Engineering, Omdurman Islamic University, Omdurman, SudanFollow
Khalid Hamid, Department of Electrical Engineering, Omdurman Islamic University, Omdurman, SudanFollow
Salah Hagahmoodi, Department of Information Technology, College of Computer Science and Information Technology, Almoughtarbeen University, SudanFollow
Elmuntaser Hassan, Department of Computer Science, Al-Neelain University, Khartoum, SudanFollow

Article Type

Article

Abstract

This study examines spectral efficiency (SE) and throughput throughout a spectrum of user densities (from 50 to 1000 users), user mobility speeds (0 to 350 km/h), latency, packet loss, and fairness index, within a wide range of signal-to-noise ratios (SNRs). The analysis includes a number of different situations, such as (i) cooperative non-orthogonal multiple access (NOMA) with massive multiple-input multiple-output (mMIMO), (ii) mMIMO cooperative NOMA integrated with cognitive radio (CR), and (iii) CR-assisted mMIMO cooperative NOMA enhanced with reconfigurable intelligent surfaces (RIS). All of these are part of 6G millimeter-wave (mmWave) networks. The study investigates the enhancement of latency, packet loss, and fairness index in proposed systems by a unique approach that dynamically optimizes power distribution via a Q-learning algorithm. The mathematical clarification of each equation provides a comprehensive understanding of signal reception by users, the dynamics and implications of CR, and the influence of intelligent RIS optimization on it. The results show that adding the IRS improves resource allocation, makes users perform better in crowded areas, lowers latency and packet loss, and raises the fairness index by reducing interference and making channel access more efficient, especially when using the suggested optimization algorithm. The results help 6G technology move further with scalable and efficient communication networks.

Keywords

Cooperative NOMA, Massive MIMO, CR, Reconfigurable intelligent surfaces (RIS), SE, Millimeter wave (mmWave)

Recommended Citation

Hassan, Mohamed; Hamid, Khalid; Hagahmoodi, Salah; and Hassan, Elmuntaser (2025) "Dynamic RIS-Enabled Massive MIMO NOMA Systems Power Allocation Optimization for 6G Using Machin Learning Approach," Al-Esraa University College Journal for Engineering Sciences: Vol. 7: Iss. 11, Article 10.
DOI: https://doi.org/10.70080/2790-7732.1065