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Abstract
This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.
Original language | English |
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Title of host publication | GLOBECOM 2022 - 2022 IEEE Global Communications Conference |
Publisher | IEEE |
Pages | 3641-3646 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-3540-6 |
ISBN (Print) | 978-1-6654-3541-3 |
DOIs | |
Publication status | Published - 11 Jan 2023 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Global Communications Conference - Rio de Janeiro, Brazil Duration: 4 Dec 2022 → 8 Dec 2022 |
Conference
Conference | IEEE Global Communications Conference |
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Abbreviated title | GLOBECOM |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 04/12/2022 → 08/12/2022 |
Keywords
- Performance evaluation
- Deep learning
- Base stations
- Q-learning
- Handover
- Autonomous aerial vehicles
- Trajectory
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- 1 Finished
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5G!Drones: Unmanned Aerial Vehicle Vertical Applications' Trials Leveraging Advanced 5G Facilities
Taleb, T., Abada, A., Bekkouche, O., Dang, Y., Manner, J., Hireche, O., Rajasekaran, A., Hellaoui, H., Khennouche, H., Meles, M., Shahbaztabar, D., Knuuttila, O. & Sehad, N.
01/06/2019 → 30/11/2022
Project: EU: Framework programmes funding