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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Ye, Yuanfan (author) / Lei, Ming (author) / Zhao, Minjian (author)


    Publication date :

    2021-09-01


    Size :

    298633 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Federated DDQN-Based Frequency Hopping Strategy for FANETs in Complex Jamming Environments

    Tao, Wenxuan / Zhang, Ningbo / Sun, Teng et al. | IEEE | 2024



    Mobile Edge Computing in FANET

    Wang, Jingjing / Jiang, Chunxiao | Springer Verlag | 2021


    Communication Channels in FANET

    Wang, Jingjing / Jiang, Chunxiao | Springer Verlag | 2021


    Cooperative Resource Allocation in FANET

    Wang, Jingjing / Jiang, Chunxiao | Springer Verlag | 2021