Drone Base Station Positioning and Power Allocation Using Reinforcement Learning

de Paula Parisotto, R., Valente Klaine, P. , Nadas, J. P.B., Demo Souza, R., Brante, G. and Imran, M. A. (2019) Drone Base Station Positioning and Power Allocation Using Reinforcement Learning. In: 16th International Symposium on Wireless Communication Systems (ISWCS 2019), Oulu, Finland, 27-30 Aug 2019, pp. 213-217. ISBN 9781728125275 (doi: 10.1109/ISWCS.2019.8877247)

187803.pdf - Accepted Version



Large scale natural disasters can cause unpredictable losses of human lives and man-made infrastructure. This can hinder the ability of both survivors as well as search and rescue teams to communicate, decreasing the probability of finding survivors. In such cases, it is crucial that a provisional communication network is deployed as fast as possible in order to re-establish communication and prevent additional casualties. As such, one promising solution for mobile and adaptable emergency communication networks is the deployment of drones equipped with base stations to act as temporary small cells. In this paper, an intelligent solution based on reinforcement learning is proposed to determine the best transmit power allocation and 3D positioning of multiple drone small cells in an emergency scenario. The main goal is to maximize the number of users covered by the drones, while considering user mobility and radio access network constraints. Results show that the proposed algorithm can reduce the number of users in outage when compared to a fixed transmit power approach and that it is also capable of providing the same coverage, with lower average transmit power and using only half of the drones necessary in the case of fixed transmit power.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Battistella Nadas, Joao and Valente Klaine, Mr Paulo
Authors: de Paula Parisotto, R., Valente Klaine, P., Nadas, J. P.B., Demo Souza, R., Brante, G., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2019 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
3007250Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy