Research on an AI-Based Cloud Platform for 5G Base Station Energy Consumption Supervision

Xuejie Ma; Hui Gong; Xiaohong Wang

doi:10.54254/2755-2721/2025.28286

Applied and Computational EngineeringOpen access

Research on an AI-Based Cloud Platform for 5G Base Station Energy Consumption Supervision

Research Article

Open Access

Research on an AI-Based Cloud Platform for 5G Base Station Energy Consumption Supervision

Xuejie Ma ^1* Hui Gong ², Xiaohong Wang ³

¹ 山东工程职业技术大学

² 山东工程职业技术大学

³ 山东工程职业技术大学

^*Corresponding author: 18668900612@163.com

Published on 22 October 2025

ACE Vol.197

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-465-6

ISBN (Online): 978-1-80590-466-3

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Abstract

This paper addresses the issue of energy consumption management in 5G base stations and proposes a solution in the form of an AI-based energy supervision cloud platform. Leveraging cloud computing and artificial intelligence technologies, the platform enables real-time monitoring, intelligent analysis, and optimized control of 5G base station energy consumption. By analyzing the characteristics of 5G base station energy use and supervisory requirements, the overall architecture and key modules of the platform are designed. The platform employs deep learning for energy consumption prediction and anomaly detection, and integrates reinforcement learning to achieve energy-saving control. Experimental results indicate that the platform effectively reduces energy consumption in 5G base stations, improves energy utilization efficiency, and provides strong support for the green and sustainable development of 5G networks.

Keywords:

5G base station, energy consumption supervision, artificial intelligence, cloud platform

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References

[1]. China Mobile. (2020, September 3). 5G base station energy-saving technology white paper [White paper]. Retrieved December 21, 2022, from https: //www.xdyanbao.com/doc/tnwqgv16jh?bd_vid=10629406145017760696

[2]. China Telecom. (2018, June 26). 5G technology white paper [White paper]. Retrieved December 21, 2022, from http: //www.chinatelecom.com.cn/2018/ct5g/201806/P020180626325489312555.pdf

[3]. Qi, X. Y. (2023). Exploration of energy-saving methods for 5G base stations. Jiangsu Communications, 39(02), 24–28.

[4]. Gao, Z. Y., Xing, J. C., Zhang, X. W., et al. (2024). Study on building energy consumption prediction based on attention mechanism CNN-LSTM. HVAC, 54(8), 48–55.

[5]. Zheng, W. B., Zong, L. S., & Zhu, P. F. (2023). Energy-saving and intelligent exploration of power supply technology for 5G communication base stations. Yangtze Information and Communication, 36(12), 221–223.

References

[2]. China Telecom. (2018, June 26). 5G technology white paper [White paper]. Retrieved December 21, 2022, from http: //www.chinatelecom.com.cn/2018/ct5g/201806/P020180626325489312555.pdf

[3]. Qi, X. Y. (2023). Exploration of energy-saving methods for 5G base stations. Jiangsu Communications, 39(02), 24–28.

[4]. Gao, Z. Y., Xing, J. C., Zhang, X. W., et al. (2024). Study on building energy consumption prediction based on attention mechanism CNN-LSTM. HVAC, 54(8), 48–55.

Cite this article

Ma,X.;Gong,H.;Wang,X. (2025). Research on an AI-Based Cloud Platform for 5G Base Station Energy Consumption Supervision. Applied and Computational Engineering,197,9-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 7th International Conference on Computing and Data Science

ISBN: 978-1-80590-465-6(Print) / 978-1-80590-466-3(Online)

Editor: Marwan Omar

Conference website: https://2025.confcds.org/

Conference date: 25 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.197

ISSN: 2755-2721(Print) / 2755-273X(Online)

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