Dynamic Energy Management in 5G and Beyond Wireless Networks Using Reinforcement Learning

Abstract

As the demand for high-speed, low-latency connectivity escalates, fifth generation (5G) and emerging sixth generation (6G) networks face significant challenges in managing energy consumption while maintaining performance standards. This paper investigates the application of Reinforcement Learning (RL) for dynamic energy management in these advanced wireless networks. We propose an RL based framework that enables Base Stations (BS) to adaptively adjust their operational states-such as active, idle, or sleep modes based on real-time network conditions and traffic demands. The objective is to minimize total network energy consumption without compromising Quality of Service (QoS) metrics such as latency and Service-Level Agreement (SLA) compliance. Through simulations, we demonstrate that our RL approach outperforms traditional static and heuristic methods in reducing energy consumption and enhancing network efficiency. The findings underscore the potential of RL in facilitating intelligent, energy-aware operations in next-generation wireless networks.