Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks

Abubakar, A., Ozturk, M., Hussain, S. and Imran, M. (2019) Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks. In: 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Limassol, Cyprus, 11-13 Sep 2019, ISBN 9781728110165 (doi:10.1109/CAMAD.2019.8858474)

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Abstract

Cell switching has been identified as a major approach to significantly reduce the energy consumption of Heterogeneous Networks (HetNets). The main idea behind cell switching is to turn off idle or lightly loaded Base Stations (BSs) and to offload their traffic to neighbouring active cell(s). However, the impact of the offloaded traffic on the power consumption of the neighbouring cell(s) has not been studied sufficiently in the literature, thereby leading to the development of sub-optimal cell switching mechanisms. In this work, we first considered a Control/Data Separated Architecture (CDSA) with a macro cell serving as the Control Base Station (CBS) and multiple small cells as Data Base Stations (DBS). Then, a Q-learning assisted cell switching algorithm is developed in order to determine the small cells to switch off by considering the increase in power consumption of the macro cell due to offloaded traffic from the sleeping cells. The capacity of the macro cell is also taken into consideration to ensure that the Quality of Service (QoS) requirements of users are maintained. Simulation results show that the proposed cell switching algorithm can achieve up to 50% reduction in the total energy consumption of the considered HetNet scenario.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abubakar, Attai and Imran, Professor Muhammad and OZTURK, Metin and Hussain, Dr Sajjad
Authors: Abubakar, A., Ozturk, M., Hussain, S., and Imran, M.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2378-4873
ISBN:9781728110165
Copyright Holders:Copyright © 2019 IEEE
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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