Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9246
Title: UAV-BS Trajectory Optimization under Coverage, Backhaul and QoS Constraints Using Q-Learning
Authors: Sazak, M.D.
Demirtas, A.M.
Keywords: backhaul
base station
coverage
QoS
reinforcement learning
UAV
Antennas
Base stations
Quality of service
Trajectories
Transmissions
Unmanned aerial vehicles (UAV)
Aerial vehicle
Backhaul
Coverage
Ground base stations
Q-learning
Quality-of-service
Reinforcement learnings
Service requirements
Total transmission rate
Unmanned aerial vehicle
Reinforcement learning
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this study, three-dimensional optimal trajectory planning is performed for the Unmanned Aerial Vehicle (UAV) with the base station (BS) attached to it to increase the service provided to the users. The case that heterogeneous quality of service (QoS) requirements for different users are considered. While planning the trajectory, the coverage area of the UAV-BS and backhaul capacity between the UAV-BS and Ground Base Station (GBS) are limited. Under these constraints, the aim is to find a trajectory for the UAV-BS that maximizes the total data rate provided to the users during the flight using reinforcement learning. With the application of Q-learning in our problem, the UAV-BS learns to take action to achieve the desired goal. As a result of trial and error processes with different learning parameters, appropriate parameters are determined and a reinforcement learning model is trained. Different communication scenarios are compared for analyzing the effects of the constraints. According to the effects of the mentioned constraints and heterogeneous QoS demands, UAV-BS's trajectory preferences and total transmission rate changes are examined. Three prominent results shows the effects of coverage, backhaul, and heterogeneous QoS. The UAV-BS tends to increase its altitude as the coverage constraint increases. Moreover, the backhaul constraint forces the UAV-BS's trajectory closer to the GBS. Lastly, UAV-BS takes into account different QoS requirements of users as much as possible. UAV-BS maximizes the total transmission rate by determining the most suitable trajectory to meet these constraints. © 2022 IEEE.
Description: 2022 International Balkan Conference on Communications and Networking, BalkanCom 2022 -- 22 August 2022 through 24 August 2022 -- 183326
URI: https://doi.org/10.1109/BalkanCom55633.2022.9900816
https://hdl.handle.net/20.500.11851/9246
ISBN: 9781665487641
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

2
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.