Backhaul aware user-specific cell association using Q-Learning

Valente Klaine, P. , Jaber, M., Souza, R. D. and Imran, M. A. (2019) Backhaul aware user-specific cell association using Q-Learning. IEEE Transactions on Wireless Communications, 18(7), pp. 3528-3541. (doi:10.1109/TWC.2019.2915083)

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Abstract

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Valente Klaine, Mr Paulo
Authors: Valente Klaine, P., Jaber, M., Souza, R. D., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Wireless Communications
Publisher:IEEE
ISSN:1536-1276
ISSN (Online):1558-2248
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Transactions on Wireless Communications 18(7):3528-3541
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3007250Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy