Work

Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users

Year: 2022

Type: conference-paper

Abstract: In this paper, we study the localization of ground users by utilizing unmanned aerial vehicles (UAVs) as aerial anchors. Specifically, we introduce a novel localization framework based on Federated Learning (FL) and Reinforcement Learning (RL). In contrast to the existing literature, our scenario includes multiple UAVs learning the trajectory in different environment settings which results in faster convergence of RL model for minimum localization error. Furthermore, to evaluate the learned trajectory from the aggregated model, we test the trained RL agent in an alternative environment which shows the improvement over the localization error and convergence speed. Simulation results show that our proposed framework outperforms a model trained with transfer learning by %30.

Source: European Signal Processing Conference (EUSIPCO)

DOI: 10.23919/eusipco55093.2022.9909614

Authors: Arzhang Shahbazi, Igor Donevski, Jimmy Jessen Nielsen, Marco Di Renzo

Institutions: Centre National de la Recherche Scientifique, Université Paris-Saclay, Laboratoire des signaux et systèmes, CentraleSupélec, Aalborg University

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Horizon 2020 Framework Programme

Award: G7464280346