Abstract

The evolution toward sixth-generation (6G) communication systems demands advanced multiple access techniques capable of meeting stringent requirements for massive connectivity, ultra-low latency, and high spectral efficiency. Non-Orthogonal Multiple Access (NOMA) has emerged as a promising candidate, enabling simultaneous access for multiple users by sharing the same frequency resources with different power levels. However, efficient power allocation and ensuring fairness among users remain critical challenges. Traditional optimization based methods often face high computational complexity and limited adaptability to dynamic environments, making them less suitable for real-time applications. This study introduces an AI-driven framework for power allocation and fairness optimization in NOMA-enabled 6G networks. The proposed method employs machine learning models to predict optimal power allocation strategies by learning from dynamic user distributions, channel state information, and traffic demands. Unlike conventional schemes, the AI model adaptively balances system throughput and user fairness, reducing the risk of resource monopolization by users with favorable channel conditions. Experimental evaluations demonstrate that the proposed framework achieves up to 18% improvement in spectral efficiency and 22% better fairness index compared to conventional water-filling and heuristic based allocation methods. Additionally, the machine learning approach reduces computation time by nearly 30%, making it viable for real-time deployment in ultra-dense 6G environments. These results highlight the potential of integrating AI with NOMA to enhance the robustness and intelligence of next-generation communication systems.

Authors

N. Vijayaraghavan¹, R. Thiagarajan²
Prathyusha Engineering College, India¹, Veltech Multitech Dr Rangarajan Dr Sakunthala Engineering College, India²

Keywords

NOMA, 6G, Power Allocation, User Fairness, Machine Learning