Seamless Replacement of UAV-BSs Providing Connectivity to the IoT

Hamed Hellaoui, Bin Yang, Tarik Taleb, Jukka Manner

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)


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 languageEnglish
Title of host publicationGLOBECOM 2022 - 2022 IEEE Global Communications Conference
Number of pages6
ISBN (Electronic)978-1-6654-3540-6
ISBN (Print)978-1-6654-3541-3
Publication statusPublished - 11 Jan 2023
MoE publication typeA4 Conference publication
EventIEEE Global Communications Conference - Rio de Janeiro, Brazil
Duration: 4 Dec 20228 Dec 2022


ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
CityRio de Janeiro


  • Performance evaluation
  • Deep learning
  • Base stations
  • Q-learning
  • Handover
  • Autonomous aerial vehicles
  • Trajectory


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