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SINR-based resource allocation for rate-maximization in downlink power-domain non-orthogonal multiple access (NOMA) network

algorithms-and-analysis-of-algorithms
computer-networks-and-communications
software-engineering

Abstract

Non-Orthogonal Multiple Access (NOMA) has emerged as a cornerstone technology for fifth-generation (5G) wireless networks and beyond, primarily due to its significant enhancement of spectral efficiency, massive connectivity, and robust quality-of-service (QoS) provisioning. By employing superposition coding and Successive Interference Cancellation (SIC), NOMA enables multiple users to share identical frequency-time resources simultaneously. However, efficient implementation of NOMA critically relies on sophisticated resource allocation strategies, specifically user-subchannel pairing and power distribution, to manage interference and optimize performance. Existing methods predominantly utilize static or heuristic channel gain-based pairing approaches, neglecting real-time interference dynamics reflected by the Signal-to-Interference-plus-Noise Ratio (SINR), thus limiting optimal system throughput and fairness. To address these challenges, we propose a novel SINR-based resource allocation framework for downlink power-domain NOMA systems aimed at maximizing the sum data rate. Our methodology introduces a two-stage optimization approach: initially, we implement an SINR-based user-subchannel matching algorithm utilizing a modified Gale-Shapley procedure, dynamically generating preference lists based on real-time SINR evaluations; subsequently, we employ Difference-of-Convex (DC) programming for power allocation, effectively decomposing the original non-convex optimization problem into tractable convex subproblems. Rigorous mathematical analysis, including convexity proofs and eigenvalue computations, validates the robustness of the proposed DC optimization method. Simulation results demonstrate substantial improvements in throughput, fairness, and scalability compared to traditional gain-based and heuristic strategies, thereby confirming the effectiveness and practical feasibility of our proposed SINR-based DC framework for future advanced wireless network deployments.
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