H. Hellaoui, B. Yang, T. Taleb and J. Manner, "Seamless Replacement of UAV-BSs Providing Connectivity to the IoT," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 3641-3646, doi: 10.1109/GLOBECOM48099.2022.10001699
Seamless replacement of UAV-BSs providing connectivity to the IoT
|Author:||Hellaoui, Hamed1; Yang, Bin2; Taleb, Tarik3;|
1Communications and Networking Department, Aalto University, Finland
2Chuzhou University, School of Computer and Information Engineering, China
3University of Oulu, Centre for Wireless Communications, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023051143523
|Publish Date:|| 2023-05-11
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.
|Pages:||3641 - 3646|
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
IEEE Global Communications Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the European Union’s Horizon 2020 Research and Innovation Program through the 5G!Drones Project under Grant No. 857031.
|EU Grant Number:||
(857031) 5G!Drones - Unmanned Aerial Vehicle Vertical Applications’ Trials Leveraging Advanced 5G Facilities
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.