Paper Contents
Abstract
The advent of 5G networks has revolutionized mobile communication, promising unprecedented data rates and connectivity. However, this rapid expansion poses significant challenges, particularly concerning energy consumption, as small cell base stations (SBSs) become increasingly prevalent to meet the growing demand for mobile data. This report delves into the core contributions of the paper, which introduces a sophisticated framework that integrates machine learning algorithms to optimize the operational states of SBSs. The proposed mechanism employs advanced sleep mode functionalities, allowing SBSs to dynamically transition between active and sleep states based on real-time user activity and traffic patterns. By classifying small cells into distinct categoriessuch as SLEEP MODE, ACTIVE, and FULL LOADbased on various metrics including user count, traffic load, and energy consumption, the model facilitates intelligent decision-making processes that significantly reduce energy waste during periods of low demand. The analysis presented in the paper is grounded in extensive simulations that evaluate the performance of the proposed ML model against traditional energy management strategies. Results indicate that the implementation of this mechanism can lead to energy savings of up to 70%, while simultaneously ensuring that the quality of service (QoS) remains intact for users.
Copyright
Copyright © 2025 Pravin Shankar Ingale. This is an open access article distributed under the Creative Commons Attribution License.
