To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network’s connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs’ paths. Simulation results show the effectiveness of this two-step approach for UAV networks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning


    Beteiligte:


    Erscheinungsdatum :

    01.12.2020


    Format / Umfang :

    450332 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Performance Analysis for Cellular‐Connected UAVs

    Mahdi Azari, M. / Rosas, Fernando / Pollin, Sofie | Wiley | 2020



    Path Planning for Multiple UAVs

    Tsourdos, Antonios / White, Brian / Shanmugavel, Madhavan | Wiley | 2010


    Probabilistic Path Planning for UAVs

    Dogan, Atilla | AIAA | 2003