Ensuring QoS for NOMA Using GA Based Power Allocation Scheme with a Variant of Greedy Heuristic Pairing Algorithm

Non-Orthogonal Multiple Access (NOMA) is emerging as a pivotal enabling mechanism for next-generation wireless communication systems. Due to its advanced integration capabilities, NOMA is a foundational and transformative access technology, which is key in addressing the evolving demands of upcoming network architecture. By effectively optimizing system parameters, NOMA can significantly contribute to green communication by reducing energy consumption and enhancing resource utilization efficiency. This article proposes an effective Multi-Objective Genetic Algorithm (MOGA)-based power allocation strategy integrated with a variant of the Greedy Heuristic Pairing Algorithm for Single-Carrier Non-Orthogonal Multiple Access (SC-NOMA) downlink systems, aiming to ensure user-oriented Quality of Service (QoS). To address this, the proposed scheme optimizes power allocation by exploiting MOGA, which simultaneously enhances the overall system sum rate and ensures fairness among users. User pairing is conducted through a Greedy Heuristic Algorithm, which strategically clusters users based on maximum differences in channel gains, thereby effectively exploiting the advantages of power-domain multiplexing. Extensive analytical and Monte Carlo simulation results confirm the effectiveness of the proposed methodology, demonstrating substantial improvements in outage performance and QoS guarantees for both cell-edge and cell-centred users. It is also revealed that when optimized via MOGA, three-user clustering provides better outage performance even at lower Signal-to-Noise Ratios (SNRs) compared to conventional two-user pairing schemes. This research paves the way for future exploration of advanced multi-objective evolutionary algorithms to enhance the NOMA system's efficiency and reliability.