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Energy and Quality Aware Multi-UAV Flight Path Design Through Q-Learning Algorithms

Abstract : We address the problem of devising an optimized energy aware flight plan for multiple Unmanned Aerial Vehicles (UAVs) mounted Base Stations (BS) within heterogeneous networks. The chosen approach makes use of Q-learning algorithms, through the definition of a reward related to relevant quality and battery consumption metrics, providing also service overlapping avoidance between UAVs, that is two or more UAVs serving the same cluster area. Numerical simulations and different training show the effectiveness of the devised flight paths in improving the general quality of the heterogeneous network users.
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https://hal.inria.fr/hal-02881735
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Submitted on : Friday, June 26, 2020 - 8:43:58 AM
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Hend Zouaoui, Simone Faricelli, Francesca Cuomo, Stefania Colonnese, Luca Chiaraviglio. Energy and Quality Aware Multi-UAV Flight Path Design Through Q-Learning Algorithms. 17th International Conference on Wired/Wireless Internet Communication (WWIC), Jun 2019, Bologna, Italy. pp.246-257, ⟨10.1007/978-3-030-30523-9_20⟩. ⟨hal-02881735⟩

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