TERM: Throughput Efficient Resource Management Scheme for 5G Communication Using NOMA Systems

The rapid expansion of Internet of Things (IoT) networks necessitates intelligent and adaptive resource management to address challenges such as high interference, path loss, and inefficient power allocation.The Existing resource management (ERM) schemes often fail to optimize performance in dynamic environments, leading to degraded sum throughput and reliability.To overcome these limitations, this study proposes the Throughput Enhanced Resource Management (TERM) scheme, which leverages machine learning-driven predictive resource allocation and adaptive power control to optimize network performance.Unlike conventional methods, TERM incorporates an XGBoost-based prediction model to intelligently allocate resources, while utilizing advanced signal processing techniques to mitigate interference and enhance throughput.Simulation results demonstrate that TERM achieves a 23.37% improvement in average sum throughput, a 31.34%reduction in average outage probability, and an average 87.55% reduction in average BER compared to existing approaches presented in recent years.These findings validate TERM's effectiveness in enhancing communication reliability, making it a promising solution for next-generation IoT networks.