Energy efficiency analysis of drone small cells positioning based on reinforcement learning

Flávia dos Reis, A., Brante, G., Parisotto, R., Souza, R. D., Valente Klaine, P. H. , Battistella, J. P. and Imran, M. A. (2020) Energy efficiency analysis of drone small cells positioning based on reinforcement learning. Internet Technology Letters, 3(5), e166. (doi: 10.1002/itl2.166)

Abstract

This work proposes an algorithm to optimize the positioning and the transmit power of Drone Small Cells (DSCs) based on Q ‐learning, a reinforcement learning technique where the agents learn to maximize a given reward. We consider two different rewards in this work, the first focusing on maximizing the network coverage, while the second maximizes the lifetime. Then, the Q ‐learning solution determines the best positioning of the DSC in the 3D space, as well as the optimal transmit power. Results show that the optimization of the transmit power is of paramount importance to reduce the outage probability. In addition, we show that the second reward can considerably increase the network lifetime with a small penalty to the coverage.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Battistella Nadas, Joao and Valente Klaine, Mr Paulo
• Authors: Flávia dos Reis, A., Brante, G., Parisotto, R., Souza, R. D., Valente Klaine, P. H., Battistella, J. P., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Internet Technology Letters
• Publisher: Wiley
• ISSN: 2476-1508
• ISSN (Online): 2476-1508
• Published Online: 30 April 2020
• Copyright Holders: Copyright © 2020 John Wiley and Sons Ltd.
• First Published: First published in Internet Technology Letters 3(5): e166
• Publisher Policy: Reproduced in accordance with the publisher copyright policy