Load-Aware Cell Switching in Ultra-Dense Networks: An Artificial Neural Network Approach

Abubakar, A. I. , Öztürk, M., Rais, R. N. B., Hussain, S. and Imran, M. A. (2020) Load-Aware Cell Switching in Ultra-Dense Networks: An Artificial Neural Network Approach. In: 5th International Conference on UK - China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205365)

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Abstract

Most online cell switching solutions are sub-optimal because they are computationally demanding, and thus adapt slowly to a dynamically changing network environments, leading to quality-of-service (QoS) degradation. This makes such solutions impractical for ultra-dense networks (UDN) where the number of base stations (BS) deployed is very large. In this paper, an artificial neural network (ANN) based cell switching solution is developed to learn the optimal switching strategy of BSs in order to minimize the total power consumption of a UDN. The proposed model is first trained offline, after which the trained model is plugged into the network for real-time decision making. Simulation results reveal that the performance of the proposed solution is very close to the optimal solution in terms of trade-off between the power consumption and QoS.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abubakar, Mr Attai and Imran, Professor Muhammad and Öztürk, Metin and Hussain, Dr Sajjad
Authors: Abubakar, A. I., Öztürk, M., Rais, R. N. B., Hussain, S., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781728194882
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300725Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)Uncle 12187 - EP/P028764/ENG - Systems Power & Energy