In this work, we investigate a trajectory design problem in uplink unmanned aerial vehicles (UAVs)-enabled data collection system for massive time-sensitive Internet of Things (IoT) services. Although UAV has the advantages of automatic maneuverability and flexible mobility, it is challenging to guarantee the information freshness of collected data under the limited flying energy constraint. Thus we employ Age of Information (AoI) as a new metric to characterize the information freshness and formulate a joint power control and trajectory design optimization problem to minimize average AoI. In order to solve this non-convex problem, we decompose it as a power control subtask and trajectory design subtask, and propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve the subtasks with independent state space, action space and reward function. Simulation results show that the proposed scheme can obtain better performance gain compared to the benchmark scheme and has the superior stability under different settings.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Joint Power Control and UAV Trajectory Design for Information Freshness via Deep Reinforcement Learning


    Beteiligte:
    Li, Xinmin (Autor:in) / Yin, Baolin (Autor:in) / Yan, Jiaxin (Autor:in) / Zhang, Xiaoqiang (Autor:in) / Wei, Ran (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    1162517 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach

    Abedin, Sarder Fakhrul / Munir, Md. Shirajum / Tran, Nguyen H. et al. | IEEE | 2021


    Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control

    Frauenknecht, Bernd / Ehlgen, Tobias / Trimpe, Sebastian | IEEE | 2023


    Learning to Schedule Joint Radar-Communication Requests for Optimal Information Freshness

    Lee, Joash / Niyato, Dusit / Guan, Yong Liang et al. | IEEE | 2021


    LEARNING TO SCHEDULE JOINT RADAR-COMMUNICATION REQUESTS FOR OPTIMAL INFORMATION FRESHNESS

    Lee, Joash / Niyato, Dusit / Guan, Yong Liang et al. | British Library Conference Proceedings | 2021


    Deep Reinforcement Learning based Aggressive Flight Trajectory Tracker

    Shadeed, Omar / Hasanzade, Mehmet / Koyuncu, Emre | AIAA | 2021