MODELING AND ANALYSIS OF COMPREHENSIVE METHODS FOR ENHANCING ENERGY EFFICIENCY IN IOT-ENABLED 5G MOBILE NETWORKS

Abstract

This paper examines the modeling and analysis of comprehensive methods for enhancing energy efficiency in IoT-enabled 5G mobile networks. The rapid growth in the number of Internet of Things (IoT) devices and the widespread adoption of Massive Machine-Type Communications (mMTC) scenarios lead to increased traffic complexity in mobile networks and a significant rise in energy consumption. The study involved a systematic analysis of IoT traffic characteristics, load-dependent base station operating modes, and energy-saving technologies. To enhance energy efficiency, various techniques were investigated, including sleep modes, Cell Zooming, Massive MIMO, beamforming, edge computing, and AI-based control algorithms. Additionally, a mathematical model incorporating IoT traffic was proposed, and simulation modeling was conducted using MATLAB. The results demonstrate that energy consumption in IoT scenarios increases significantly; however, the application of the proposed comprehensive management approaches enables a 20 – 25% reduction in energy expenditure. These findings hold practical significance for ensuring energy efficiency during the design of IoT-enabled 5G networks and future 6G networks.