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  • Open Access

    ARTICLE

    Mobility-Aware Edge Caching with Transformer-DQN in D2D-Enabled Heterogeneous Networks

    Yiming Guo, Hongyu Ma*

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025

    Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

  • Open Access

    ARTICLE

    Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

    Guangjun Liang1,2, Jianfang Xin3,*, Linging Xia1, Xueli Ni1,4, Yi Cao5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2981-2998, 2023, DOI:10.32604/cmc.2023.028597 - 31 October 2022

    Abstract In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data… More >

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