Performance Enhancement through Optimal Power Allocation for Downlink PD-NOMA-UFMC in 5G Networks using Genetic algorithm and MFO

Abstract With the rise of Fifth generation (5G) communication systems, there's a growing demand for efficient multiple access techniques capable of handling high data rates and accommodating a large user base. Power Domain Non-Orthogonal Multiple Access (PD-NOMA) combined with Universal Filtered Multi-Carrier (UFMC) has emerged as a promising solution to address these needs. This combination aims to enhance spectral efficiency, capacity, and user fairness in 5G networks by capitalizing on the strengths of both PD-NOMA and UFMC. Nevertheless, the efficient allocation of power among users poses an optimization challenge within the confines of a specified power budget to achieve optimal performance in PD-NOMA-UFMC systems. This paper presents a power optimization methodology for PD-NOMA-UFMC utilizing intelligent optimization techniques, namely Genetic Algorithm (GA) and Moth Flame Optimization (MFO) algorithm. The proposed approach aims to maximize the overall system throughput by dynamically assigning power levels to users based on their individual channel conditions. Simulation results showcase the effectiveness of the power optimization technique employing GA and MFO in enhancing the performance of PD-NOMA-UFMC systems in terms of throughput, fairness, and power efficiency. Additionally, the paper discusses comparative results of optimization using GA and MFO.