Energy Efficiency Optimization and Low-Carbon Operation of 5G Base Stations Toward Carbon Neutrality

Abstract

The large-scale rollout of 5G has sharply increased base station energy use, making it a major source of carbon emissions in communications. Against the background of China's dual-carbon targets, investigating low-carbon operation strategies for 5G base stations is of both theoretical significance and practical value. This paper analyzes the energy consumption composition and operational behavior of 5G base stations, establishes an energy consumption model based on traffic load, and proposes an integrated low-carbon operation framework that includes hardware energy optimization, software-driven consumption reduction, alternative energy adoption, and intelligent monitoring and control. On this basis, it investigates the principles and applicability of software-based energy-saving methods like symbol shutdown, channel shutdown, deep sleep, and carrier shutdown, as well as energy-side strategies such as coordination between base stations and smart grids, integration of solar and wind power, and optimized energy storage scheduling. Furthermore, an AI-based traffic prediction and dynamic energy saving scheduling algorithm is designed, together with a layered, coordinated intelligent management architecture. The results show that, by combining multidimensional technological coordination with intelligent management, 5G base stations can reduce overall energy consumption by 30%-45%, thereby providing technical support for the low-carbon transition of communication networks.