Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks

Tan, K. , Bremner, D. , Le Kernec, J. , Sambo, Y. , Zhang, L. and Imran, M. A. (2022) Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks. Scientific Reports, 12, (doi: 10.1038/s41598-022-25800-3) (PMID:36517543) (PMCID:PMC9751127)

Abstract

The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sambo, Dr Yusuf and Zhang, Professor Lei and Imran, Professor Muhammad and Tan, Kang and Bremner, Dr Duncan and Le Kernec, Dr Julien
• Authors: Tan, K., Bremner, D., Le Kernec, J., Sambo, Y., Zhang, L., and Imran, M. A.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Published Online: 14 December 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Scientific Reports 12(21581)
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304481		Resource Orchestration for Diverse Radio Systems	Lei Zhang	Engineering and Physical Sciences Research Council (EPSRC)	EP/S02476X/1	ENG - Systems Power & Energy
300725		Distributed Autonomous Resilient Emergency Management System (DARE)	Muhammad Imran	Engineering and Physical Sciences Research Council (EPSRC)	Uncle 12187 - EP/P028764/	ENG - Systems Power & Energy