Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Klaine, P. V. , Nadas, J. P.B., Souza, R. D. and Imran, M. A. (2018) Distributed drone base station positioning for emergency cellular networks using reinforcement learning. Cognitive Computation, 10(5), pp. 790-804. (doi: 10.1007/s12559-018-9559-8)

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Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Valente Klaine, Mr Paulo
Authors: Klaine, P. V., Nadas, J. P.B., Souza, R. D., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Cognitive Computation
ISSN (Online):1866-9964
Published Online:22 May 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Cognitive Computation 10(5):790-804
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