Cooperative Sleep and Energy-Sharing Strategy for a Heterogeneous 5G Base Station Microgrid System Integrated with Deep Learning and an Improved MOEA/D Algorithm

Ming Yan, Tuanfa Qin (), Wenhao Guo and Yongle Hu ()
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Ming Yan: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Tuanfa Qin: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Wenhao Guo: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Yongle Hu: Runjian Co., Ltd., Nanning 530004, China

Energies, 2025, vol. 18, issue 7, 1-28

Abstract: With the rapid growth of heterogeneous fifth-generation (5G) communication networks and a surge in global mobile traffic, energy consumption in mobile network systems has increased significantly. This underscores the need for energy-efficient networks that lower operational costs and carbon emissions, leading to a focus on microgrids powered by renewable energy. However, accurately predicting base station traffic demand and optimizing energy consumption while maximizing green energy usage—especially concerning quality of service (QoS) for users—remains a challenge. This paper proposes a cooperative sleep and energy-sharing strategy for heterogeneous 5G base station microgrid (BSMG) systems, utilizing deep learning and an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). We present a reference scenario for a 5G BSMG system comprising a central and sub-base station microgrid. A prediction model was developed, integrating a convolutional neural network with a dual attention mechanism and bidirectional long short-term memory to determine the operational status of BSMGs. Our cooperative strategy addresses QoS requirements and uses the enhanced MOEA/D to improve performance. Numerical results indicate that our approach achieves significant energy savings while ensuring accurate predictions of BSMG energy demands through a multi-objective evolutionary algorithm based on decomposition.

Keywords: deep learning; 5G; traffic prediction; base station microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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