Energy optimization in ultra-dense radio access networks via traffic-aware cell switching

Ozturk, M., Abubakar, A. I. , Battistella Nadas, J. P., Rais, R. N. B., Hussain, S. and Imran, M. A. (2021) Energy optimization in ultra-dense radio access networks via traffic-aware cell switching. IEEE Transactions on Green Communications and Networking, 5(2), pp. 832-845. (doi: 10.1109/TGCN.2021.3056235)

Abstract

We propose a reinforcement learning based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Nadas, Mr Joao Pedro and Ozturk, Mr Metin and Abubakar, Mr Attai and Imran, Professor Muhammad and Hussain, Dr Sajjad
• Authors: Ozturk, M., Abubakar, A. I., Battistella Nadas, J. P., Rais, R. N. B., Hussain, S., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Transactions on Green Communications and Networking
• Publisher: IEEE
• ISSN: 2473-2400
• ISSN (Online): 2473-2400
• Published Online: 02 February 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in IEEE Transactions on Green Communications and Networking 5(2): 832-845
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
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