This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach that leverages centralized training with decentralized execution to maximize the overall sensing performance. Specifically, we implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols, therefore enhancing their environmental awareness and operational efficiency. Additionally, we augment the MARL solution with a transmission power adaptation technique to mitigate interference between the communicating drones and optimize the communication protocol efficiency. Moreover, a transmission power adaptation technique is incorporated to mitigate interference and optimize the learned communication protocol efficiency. Despite the increased complexity, our solution demonstrates robust performance and adaptability across various scenarios, providing a scalable and cost-effective enhancement for future ISAC networks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms


    Beteiligte:
    Atsu, Obed Morrison (Autor:in) / Naoumi, Salmane (Autor:in) / Bomfin, Roberto (Autor:in) / Chafii, Marwa (Autor:in)


    Erscheinungsdatum :

    17.11.2024


    Format / Umfang :

    873216 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Channel Modeling Framework for Bistatic ISAC Under 3GPP Standard

    Luo, Chenhao / Tang, Aimin / Gao, Fei et al. | IEEE | 2024


    Two-stage estimation algorithm based on interleaved OFDM for a cooperative bistatic ISAC scenario

    Leyva, Leonardo / Castanheira, Daniel / Silva, Adao et al. | IEEE | 2022


    Toward Reinforcement Learning Identification for Swarms Engaged in Cooperative Pursuit

    Sargent, Lynn H. / Coverstone, Victoria L. / Rodriguez, Nicole et al. | TIBKAT | 2022


    Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

    Bo Liu / Shulei Wang / Qinghua Li et al. | DOAJ | 2023

    Freier Zugriff

    Toward Reinforcement Learning Identification for Swarms Engaged in Cooperative Pursuit

    Sargent, Lynn H. / Coverstone, Victoria L. / Rodriguez, Nicole et al. | AIAA | 2022