Base Stations (BSs) sleeping strategy is an efficient way to obtain the energy efficiency of cellular networks. To meet the increasing demand of high-data-rate for wireless applications, small cell BSs
To address this, we propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset. Unlike existing methods, our approach integrates the Base
Multiple scientific investigations have validated the feasibility of managing power consumption in a base station, and several effective techniques have been proposed to achieve this
A joint load control based on energy sharing and dynamic on/off switching of a small base station is investigated in to reduce the grid power and efficiently utilize the renewable energy
This paper investigates on the impact of deployment strategies on the power consumption of mobile radio networks. We consider layouts featuring varying numbers of micro base stations per cell in
The AI-driven network energy saving solution can forecast the traffic load of base stations based on historical traffic load, service type, site coverage and user behaviors.
In this article, an algorithm for automatic control of energy sources was developed to improve the uninterrupted power supply of mobile communication base stations. Based on the proposed
In today''s 5G era, the energy efficiency (EE) of cellular base stations is crucial for sustainable communication. Recognizing this, Mobile Network Operators are actively prioritizing EE for both
Abstract: With the advent of the 5G era, mobile users have higher requirements for network performance, and the expansion of network coverage has become an inevitable trend. Deploying
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