Dynamic Power Allocation Strategies for Enhanced Performance in NOMA Systems

Abstract

Non-Orthogonal Multiple Access (NOMA) stands out as a promising multiple access technique for contemporary wireless communication systems, boasting higher spectral efficiency and augmented user capacity. The efficient allocation of power constitutes a pivotal factor in optimizing the sum rate and throughput of NOMA systems while concurrently alleviating interference among users. This research studies dynamic power allocation strategies geared towards optimizing the performance of NOMA systems.

Traditional power allocation methodologies in NOMA systems, including static allocation and fixed clustering, exhibit limitations in exploiting the dynamic characteristics inherent in wireless channels and user distributions. Recent research endeavours have thus shifted focus towards dynamic power allocation techniques, which dynamically adjust power allocation based on real-time channel conditions.

An overview of dynamic power allocation algorithms, encompassing Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Ant Colony Optimization (ACO), is presented. These algorithms adaptively regulate power allocation to maximize the sum rate and throughput of NOMA systems.

The results demonstrated significant improvements in the sum rate and throughput of NOMA systems. The optimization algorithm achieves a sum rate of 800 bit/sec/Hz and a throughput of 1.7Gbps/Hz. By flexibly adapting power allocation in response to evolving network conditions, these strategies optimize resource utilization and mitigate interference, thus enhancing system capacity and improving user experience