NOMA-SWIPT optimization with PSO and machine learning algorithm

This research aims to optimize energy efficiency (EE) in telecommunication systems by focusing on a combination of non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT). The Particle Swarm Optimization (PSO) algorithm is employed, leading to significant improvements in energy efficiency compared to existing methods. Our findings demonstrate that PSO effectively optimizes multiple parameters, achieving higher energy efficiency. Additionally, a Random Forest-based machine learning (ML) model is introduced to enhance optimization speed.