Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning


    Contributors:
    Lin, Xiangda (author) / Yang, Helin (author) / Lin, Kailong (author) / Xiao, Liang (author) / Shi, Zhaoyuan (author) / Yang, Wanting (author) / Xiong, Zehui (author)


    Publication date :

    2024-06-24


    Size :

    1810178 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Reinforcement Learning based Dynamic Resource Allocation Method for NOMA in AeroMACS

    Yu, Lanchenhui / Zhao, Jingjing / Zhu, Yanbo et al. | IEEE | 2024


    Energy Efficient Resource Allocation for Secure NOMA Networks

    Zhang, Haijun / Yang, Ning / Long, Keping et al. | IEEE | 2018


    Joint optimization via deep reinforcement learning for secure-driven NOMA-UAV networks

    DENG, Danhao / WANG, Chaowei / XU, Lexi et al. | Elsevier | 2025

    Free access

    Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

    Khan, Wali Ullah / Javed, Muhammad Awais / Nguyen, Tu N. et al. | IEEE | 2022