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.
| Original language | English |
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| Title of host publication | 2022 30th European Signal Processing Conference (EUSIPCO) |
| Number of pages | 4 |
| Publisher | IEEE |
| Publication date | Aug 2022 |
| Pages | 663-666 |
| Article number | 9909614 |
| ISBN (Print) | 978-1-6654-6799-5 |
| ISBN (Electronic) | 9789082797091 |
| DOIs | |
| Publication status | Published - Aug 2022 |
| Event | 30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia Duration: 29 Aug 2022 → 2 Sept 2022 |
Conference
| Conference | 30th European Signal Processing Conference, EUSIPCO 2022 |
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| Country/Territory | Serbia |
| City | Belgrade |
| Period | 29/08/2022 → 02/09/2022 |
| Series | European Signal Processing Conference |
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| Volume | 2022-August |
| ISSN | 2219-5491 |
Bibliographical note
Funding Information:This work was supported by the European Commission through the H2020 PAINLESS Project under Grant 812991 and the H2020 ARIADNE Project under Grant 871464.
Publisher Copyright:
© 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.
Keywords
- Federated Learning (FL)
- localization
- received signal strength (RSS)
- Reinforcement learning (RL)
- Unmanned aerial vehicle (UAV)