Research Article | Open Access | Download PDF
Volume 16 | Issue 2 | Year 2026 | Article Id. IJCOT-V16I2P302 | DOI : https://doi.org/10.14445/22492593/IJCOT-V16I2P302Evolution of Power Allocation Techniques in NOMA: Advancing 5G Toward 6G Networks
Lekshmi Nair M, Neelakantan PC
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 03 May 2026 | 05 Jun 2026 | 22 Jun 2026 | 08 Jul 2026 |
Citation :
Lekshmi Nair M, Neelakantan PC, "Evolution of Power Allocation Techniques in NOMA: Advancing 5G Toward 6G Networks," International Journal of Computer & Organization Trends (IJCOT), vol. 16, no. 2, pp. 8-20, 2026. Crossref, https://doi.org/10.14445/22492593/IJCOT-V16I2P302
Abstract
The transition of 5G and beyond wireless networks toward intelligence-driven and autonomous operation has revitalized strong interest in Non-Orthogonal Multiple Access (NOMA) as an efficient multiple access framework. Power allocation critically governs NOMA performance, directly impacting throughput, user fairness, and SIC effectiveness. This survey presents a focused review of power allocation strategies in NOMA, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches. In contrast to conventional strategies that require instantaneous channel state information and iterative optimization, AI/ML techniques enable adaptive, scalable, and low-latency decision-making in highly dynamic and nonconvex environments. Recent advances in reinforcement learning and deep learning for NOMA power control are discussed, highlighting key challenges like imperfect CSI, inter-cluster interference, and distributed learning constraints. This survey provides a concise AI-centric analysis and identifies promising directions for a practical learning-driven NOMA power allocation framework for future wireless networks. A consolidated, critically comparative analysis of NOMA power allocation that bridges the gap between 5G practice and 6G imperatives is also presented in this survey.
Keywords
AI/ML Based Power Allocation, Multiple Access Techniques, Noma, Optimization, Static And Dynamic Power Allocation, 6G.
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