【论文】

部分代表性成果：

1. D. Wang, D. Chen, B. Song, N. Guizani, X. Yu, and X. Du, “From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies,” IEEE Communications Magazine, vol. 56, no. 10, pp. 114-120, 2018.
2. D. Wang, B. Song, D. Chen, and X. Du, “Intelligent Cognitive Radio in 5G: AI-Based Hierarchical Cognitive Cellular Networks,” IEEE Wireless Communications, vol. 26, no. 3, pp. 54-61, 2019.
3. D. Wang, B. Song, N. Zhao, P. Lin, and F. R. Yu, “Resource Management for Secure Computation Offloading in Softwarized Cyber-Physical Systems,” IEEE Internet of Things Journal, vol. 8, no. 11, pp. 9294-9304, 2021.
4. D. Wang, W. Zhang, B. Song, X. Du, and M. Guizani, “Market-Based Model in CR-IoT: A Q-Probabilistic Multi-Agent Reinforcement Learning Approach,” IEEE Transactions on Cognitive Communications and Networking, 2021.
5. D. Wang, B. Song, P. Lin, F. R. Yu, X. Du, and M. Guizani, “Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning,” IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3137984, 2022.
6. D. Wang, B. Li, B. Song Y. Liu, K. Muhammand, and X. Zhou, “Dual-Driven Resource Management for Sustainable Computing in the Blockchain-Supported Digital Twin IoT”, IEEE Internet of Things Journal, doi:10.1109/JIOT.2022.3162714, 2022.
7. D. Wang, B. Song, Y. Liu, M. Wang, “Secure and Reliable Computation Offloading in Blockchain-assisted Cyber-Physical IoT Systems,” Digital Communications and Networks, doi: 10.1016/j.dcan.2022.05.025, 2022.
8. D. Wang, Y. Bai, G. Huang, B. Song and F. R. Yu, “Cache-Aided MEC for IoT: Resource Allocation Using Deep Graph Reinforcement Learning,”  IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3244909, 2023.
9. D. Wang, Yingjie Liu and Bin Song, “A Credible Trafffc Prediction Method Based on Self-supervised Causal Discovery”, Science China Information Sciences, Accept, 2023.
10. D. Wang, Bo Li, Bin Song, Chen Chen and Fei Richard Yu, “HSMH: A Hierarchical Sequence Multi-hop Reasoning Model with Reinforcement Learning,” IEEE Transactions on Knowledge and Data Engineering, 2023.08, Accept. 10.1109/TKDE.2023.3303617.
11. D. Wang, Yalu Bai and Bin Song, “A Knowledge Graph-based Reinforcement Learning Approach for Cooperative Caching in MEC-enabled Heterogeneous Networks”, Digital Communications and Networks, doi: 10.1016/j.dcan.2024.12.006,2025.
12. D. Wang, Keke Zhu, Bin Song and F. R. Yu, “Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning Based Stackelberg Game Approach”, IEEE Internet of Things Journal, Accepted, 2025.

论文详情见Google Scholar

【专利】

  授权专利；

1. 基于深度卷积生成式对抗网络的车牌字符识别方法，发 明人：宋彬,王丹,关韬,黄家冕，专利号：ZL201710781905.0， 授权公告日：2019 年 10 月 25 日
2. 一种 D2D 通信中联合资源分配和功率控制方法，发明人：宋彬,许珂,王丹,秦浩，专利号：ZL201910609855.7，授权公告日：2020 年 05 月 22 日
3. 一种基于深度 Q 学习的社交感知 D2D 协同缓存方法，发明人：宋彬,白雅璐,王丹；专利号：ZL20211522610. 4，授权公告日：2024 年 07 月 11 日

  申请专利：

1. 基于 ChatGPT 的联合推荐和边缘缓存模型训练方法，发明人：王丹,赵梦晗,宋彬,白雅璐，申请号：202311013361.5，申请日：2023年08月11日
2.  一种基于强化学习的知识图谱多跳推理方法，发明人：王丹,孙红,李波,宋彬等，申请号：2023111749499，申请日：2023年09月12日
3. 一种基于动态实体原型的权重网络推理方法，发明人：王丹,姚建超,宋彬,秦浩，申请号：2023104346012，申请日：2023年04月21日