The flying ad-hoc network (FANET) is widely applied to unmanned aerial vehicles (UAV s) but it is vulnerable to the frequency jamming in reality. Therefore, this paper proposes a federated deep Q-network (DQN) based frequency hopping strategy to solve the problem of periodic frequency jamming. We developed a DQN mechanism with an exploration-exploitation epsilon-greedy policy, directed by a federated learning mechanism to obtain a frequency hopping strategy. The simulation results show that our proposed algorithm has better convergence and decision accuracy performance compared with the DQN based frequency hopping strategy. And the performance will improve when the number of UAVs increases.


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    Titel :

    A New Frequency Hopping Strategy Based on Federated Reinforcement Learning for FANET


    Beteiligte:
    Ye, Yuanfan (Autor:in) / Lei, Ming (Autor:in) / Zhao, Minjian (Autor:in)


    Erscheinungsdatum :

    01.09.2021


    Format / Umfang :

    298633 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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