鲁彬彬

鲁彬彬，中共党员，江苏徐州人，博士。2019年6月和2022年6月获得山东大学学士学位和硕士学位，2025年7月获得澳门大学电脑科学博士学位。2025年8月加入河海大学信息科学与工程学院物联网工程系，任职于韩光洁院长（IEEE Fellow）团队。主要研究领域为车联网网络、数字孪生、深度强化学习、低空经济网络等，以第一作身份在高水平期刊上发表SCI/EI论文6篇。担任IEEE Vehicular Technology Conference（VTC）、International Conference on Computer Communications (INFOCOM) ICCN workshop、IEEE/CIC International Conference on Communications in China (ICCC) 等国际会议程序委员会委员，IEEE Transactions on Network Science and Engineering、IEEE Transactions on Vehicular Technology、IEEE Internet of Things Journal、China Communications等期刊审稿人。联系邮箱：binbinlu@hhu.edu.cn

代表性论文：

[1] Binbin Lu, X. Huang, Y. Wu, L. Qian, D. Niyato and C. Xu, “Cooperative Perception Aided Digital Twin Model Updating and Migration in Mixed Vehicular Networks,” IEEE Transactions on Intelligent TransportationSystems, vol. 26, no. 2, pp. 2293-2308, Feb. 2025.

[2] Binbin Lu, B. Fan, Y. Wu, L. Qian, H. Zhang and R. Lu, “Predictive Computation Offloading and Resource Allocation in DT-Empowered Vehicular Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 6, pp. 5474-5487, Jun. 2024.

[3] Binbin Lu, X. Huang, Y. Wu, L. Qian, S. Zhou and D. Niyato, “Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks,” IEEETransactions on Vehicular Technology, vol. 74, no. 5, pp. 8201-8214, May 2025.

[4] Binbin Lu, Y. Wu, L. Qian, S. Zhou, H. Zhang and R. Lu, “Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications,” IEEE Transactions on Network andService Management, vol. 21, no. 6, pp. 6744 - 6758, Dec. 2024.

[5] Binbin Lu, X. Huang, Y. Wu, L. Qian, D. Niyato, T.Q.S. Quek and C. Xu, “Digital Twin Aided Predictive Scheduling and Bandwidth Allocation for Multi-Vehicle Cooperative Perception Systems,” in 2024 IEEE99th Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, Jun. 2024, pp. 1-6.

[6] S. Guo, Binbin Lu, M. Wen, S. Dang and N. Saeed, “Customized 5G and Beyond Private Networks with Integrated URLLC, eMBB, mMTC, and Positioning for Industrial Verticals,” IEEE Communications StandardsMagazine, vol. 6, no. 1, pp. 52-57, Mar. 2022.

[7] Binbin Lu, H. Zhang, T. Xue, S. Guo and H. Gai, “Deep Reinforcement Learning-Based Power Allocation for Ultra Reliable Low Latency Communications in Vehicular Networks,” in 2021 IEEE/CIC InternationalConference on Communications in China (ICCC2021), Xiamen, China, Jul. 2021, pp. 1149-1154.