Resource Allocation Optimization of NOMA Network via Metaheuristic Algorithms

Because of the improved overall system throughput, spectrum efficiency, and user fairness, non-orthogonal multiple access (NOMA) is the next stage in telecommunication systems. NOMA breaks the orthogonality of traditional orthogonal multiple access systems by allowing several users to use the same radio resource at the same time. This paper improves the selectivity of resource allocation methods for NOMA by using the Genetic Algorithm (GA) and Grey Wolf optimization (GWO). The performance of the algorithms in simulated scenarios is evaluated using a scale based on the users’ sum rate. Furthermore, the performance of algorithms was compared in the presence and absence of interference from neighboring cells, as this influenced each algorithm’s total sum rate performance. Another comparison is made to show the override of the proposed method on orthogonal multiple access and NOMA with GA. The results show that the proposed method (NOMA using GWO) is superior to orthogonal multiple access and NOMA using GA in terms of total sum rate.