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44 ta ish

Ish: Meta-enhanced hierarchical multi-agent reinforcement learning for dynamic spectrum management and trust-based routing in cognitive vehicular networks

  1. IEEE Wireless Communications and Networking Conference

    Maqola20222 iqtibos
    ABI

  2. Energy-Efficient Channel Switching in Cognitive Radio Networks: A Reinforcement Learning Approach

    Haichuan Ding, Xuanheng Li, Ying Ma +1

    Maqola20202 iqtibos
    ABI

  3. Q-Learning-Based Spectrum Access for Multimedia Transmission Over Cognitive Radio Networks

    Xin‐Lin Huang, Yuxuan Li, Yu Gao +1

    Maqola20202 iqtibos
    ABI

  4. A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

    Somayeh Bahramnejad, Naser Movahhedinia

    Maqola20212 iqtibos
    ABI

  5. Availability-Aware Channel Allocation for Multi-Cell Cognitive Radio 5G Networks

    Rami Halloush, Rana Salaimeh, Roa'a Al-Dalqamoni

    Maqola20222 iqtibos
    ABI

  6. Channel assignment and users mobility influence on primary users QoE in Cognitive Radio Network

    Shavbo Salehi, Vahid Solouk

    Maqola20222 iqtibos
    ABI

  7. A Deep Reinforcement Learning-Based QoS Routing Protocol Exploiting Cross-Layer Design in Cognitive Radio Mobile Ad Hoc Networks

    Thong‐Nhat Tran, Toan-Van Nguyen, Kyusung Shim +2

    Maqola20222 iqtibos
    ABI

  8. Distributed robust channel allocation for clustered cognitive radio-based IoT networks using graph theory

    Syed Maaz Shahid, Sungoh Kwon

    Maqola20222 iqtibos
    ABI

  9. Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks

    Anal Paul, Kwonhue Choi

    Maqola20232 iqtibos
    ABI

  10. Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

    Muhammad Asim Saleem, Shijie Zhou, Abida Sharif +5

    Maqola20192 iqtibos
    ABI

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