Optimal Power Allocation Based on PSO for NOMA Capacity Maximization

Due to the spectral efficiency required to support the rise in connected devices, Non-Orthogonal Multiple Access (NOMA) is now regarded as the most essential issue among the many multiple access approaches. This research investigates and studies the User Pairing (UP) and Power Allocation (PA) for a downlink NOMA system. To maximize the ergodic sum capacity of the NOMA system, an optimization problem will be formulated with several constraints that guarantee the performance of the NOMA system. To solve this non-convex problem, this work presents a NOMA power allocation technique based on the Particle Swarm Optimization (PSO). PSO is an intelligent algorithm that searches from a random solution to the best one iteratively. Moreover, this algorithm is very efficient for solving non-convex optimization problems. Initially, three UP scenarios were tested to find the best UP scheme that could be used, along with the proposed optimal PA algorithm. The simulation shows that the traditional Near Far Pairing (NFP) scheme a has better performance compared to the Uniform Channel Gain Difference (UCGD) and Near-Near Far-Far (NNFF) studied in literature, when the proposed algorithm is applied. At (40 dBm), the ergodic sum capacity of the proposed optimal NFP was about (80 bps/Hz) higher than UCGD, and about (71 bps/Hz) higher than NFP with fixed PA. It is obvious that the use of PSO can solve the problem efficiently and enhance the overall NOMA system.