Enhancing Sum Spectral Efficiency and Fairness in NOMA Systems: A Comparative Study of Metaheuristic Algorithms for Power Allocation

Non-orthogonal multiple access (NOMA) is an aspiring technology for the next-generation multiple access, capable of meeting the quality of service (QoS) demands of wireless networks. It surpasses traditional orthogonal multiple access (OMA) schemes regarding sum-spectral efficiency (SSE), user fairness, and massive connectivity. In NOMA, enhanced system performance can be achieved through power allocation (PA) factor optimization. The proposed work aims to maximize SSE with enhanced user fairness in terms of the probability of outage through the optimization of PA factors. The derived non-convex optimization problem is solved using three different metaheuristic optimization algorithms, namely differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC). The analytical closed-form expression for outage probability is derived and compared with the simulation results to ensure accuracy. Among the three proposed algorithms, DE optimization maximizes the SSE by ≈ 1.8% to ≈ 10% with minimum complexity. The DE optimization reduces the transmit power requirements for the near user from ≈ 0.11 dBm to ≈ 3.79 dBm and for the far user from ≈ 0.05 dBm to ≈ 2.31 dBm, to achieve a probability of outage of 10−3, compared to other algorithms and fixed PA schemes. A high degree of accuracy is ensured in this work through the congruence between the analytical and simulation results.