Reinforcement Learning Driven Energy Efficient Mobile Communication and Applications

Asad, S. M., Ozturk, M., Rais, R. N. B., Zoha, A. , Hussain, S. , Abbasi, Q. H. and Imran, M. A. (2019) Reinforcement Learning Driven Energy Efficient Mobile Communication and Applications. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates, 10-12 Dec 2019, ISBN 9781728153414 (doi:10.1109/ISSPIT47144.2019.9001888)

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Abstract

Smart city planning is envisaged as advance technology based independent and autonomous environment enabled by optimal utilisation of resources to meet the short and long run needs of its citizens. It is therefore, preeminent area of research to improve the energy consumption as a potential solution in multi-tier 5G Heterogeneous Networks (HetNets). This article predominantly focuses on energy consumption coupled with CO 2 emissions in cellular networks in the context of smart cities. We use Reinforcement Learning (RL) vertical traffic offloading algorithm to optimize energy consumption in Base Stations (BSs) and to reduce carbon footprint by applying widely accepted strategy of cell switching and traffic offloading. The algorithm relies on a macro cell and multiple small cells traffic load information to determine the cell offloading strategy in most energy efficient way while maintaining quality of service demands and fulfilling users applications. Spatio-temporal simulations are performed to determine a cell switch on/off operation and offload strategy using varying traffic conditions in control data separated architecture. The simulation results of the proposed scheme prove to achieve reasonable percentage of energy and CO 2 reduction.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Imran, Professor Muhammad and Öztürk, Metin and Asad, Syed and Hussain, Dr Sajjad and Abbasi, Dr Qammer
Authors: Asad, S. M., Ozturk, M., Rais, R. N. B., Zoha, A., Hussain, S., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2641-5542
ISBN:9781728153414
Published Online:20 February 2020
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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