代表性论文
[1].Yudian Huang,Meng Li*, F. Richard Yu, Pengbo Si, Haijun Zhang, and Junfei Qiao, “Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method”,IEEE Transactions on Cognitive Communications and Networking, accepted, Oct. 2023. (JCR Q1)
[2].Yujie Fang,Meng Li*, F. Richard Yu, Pengbo Si, Ruizhe Yang, Chunhai Gao, and Yanhua Sun, “Parallel Offloading and Resource Optimization for Multi-Hop Ad Hoc Network-Enabled CBTC with Mobile Edge Computing”,IEEE Transactions on Vehicular Technology, accepted, Sep. 2023. (JCR Q1)
[3].Yujie Fang,Meng Li*, Pengbo Si, Ruizhe Yang, Enchang Sun, and Yanhua Zhang, “Computational Offloading and Resource Allocation for Internet of Vehicles Based on UAV-Assisted Mobile Edge Computing System”,China Communications, accepted, Jan. 2023. (JCR Q3)
[4].Yudian Huang,Meng Li*, F. Richard Yu, Pengbo Si, and Yanhua Zhang, “Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach”,IEEE Transactions on Green Communications and Networking, vol. 7, no. 3, pp. 1121-1134, Sep. 2023. (JCR Q2)
[5].Xinyu Ye,Meng Li*, Pengbo Si, Ruizhe Yang, Zhuwei Wang, and Yanhua Zhang, “Collaborative and Intelligent Resource Optimization for Computing and Caching in IoV with Blockchain and MEC Using A3C Approach”,IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 1449-1463, Feb. 2023. (JCR Q1)
[6].Meng Li, F. Richard Yu, Pengbo Si*, Yanhua Zhang, and Yi Qian, “Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning”,IEEE Network, vol. 36, no. 6, pp. 175-182, Nov./Dec. 2022. (JCR Q1)
[7].Meng Li*, Pan Pei, F. Richard Yu, Pengbo Si, Yu Li, Enchang Sun, and Yanhua Zhang, “Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach”,IEEE Internet of Things Journal, vol. 9, no. 22, pp. 23115-23129, Nov. 2022. (JCR Q1)
[8].Xinyu Ye,Meng Li*, Pengbo Si, Ruizhe Yang, Enchang Sun, and Yanhua Zhang, “Blockchain and MEC-Assisted Billing Data Transmission and Authorization over Electric Vehicular Network: A Deep Reinforcement Learning Approach”,China Communications, vol. 18, no. 8, pp. 279-296, Aug. 2021. (JCR Q3)
[9].Le Yang,Meng Li*, Pengbo Si, Ruizhe Yang, Enchang Sun, and Yanhua Zhang, “Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning”,IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2318-2329, Feb. 2021. (JCR Q1)
[10].Meng Li, F. Richard Yu, Pengbo Si*, Ruizhe Yang, Zhuwei Wang, and Yanhua Zhang, “UAV-Assisted Data Transmission in Blockchain-Enabled M2M Communications with Mobile Edge Computing”,IEEE Network, vol. 34, no. 6, pp. 242-249, Nov./Dec. 2020. (JCR Q1)
[11].Meng Li, F. Richard Yu, Pengbo Si*, Wenjun Wu, and Yanhua Zhang, “Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach”,IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9399-9412, Oct. 2020. (JCR Q1)
[12].Meng Li, F. Richard Yu, Pengbo Si*, and Yanhua Zhang, “Energy-efficient Machine-to-Machine (M2M) Communications in Virtualized Cellular Networks with Mobile Edge Computing (MEC)”,IEEE Transactions on Mobile Computing, vol. 18, no. 7, pp. 1541-1555, Jul. 2019. (JCR Q1)
[13].Meng Li, Pengbo Si, and Yanhua Zhang*, “Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks with Mobile Edge Computing in Smart City”,IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9073-9086, Oct. 2018.(JCR Q1)
[14].Meng Li, F. Richard Yu, Pengbo Si*, and Yanhua Zhang, “Green Machine-to-Machine Communications with Mobile Edge Computing and Wireless Network Virtualization”,IEEE Communications Magazine, vol. 56, no. 5, pp. 148-154, May 2018. (JCR Q1)
[15].Meng Li, F. Richard Yu, Pengbo Si*, Enchang Sun, Yanhua Zhang, and Haipeng Yao, “Random Access and Virtual Resource Allocation in Software-defined Cellular Networks with Machine-to-Machine (M2M) Communications”,IEEE Transactions on Vehicular Technology, vol. 66, no. 7, pp. 6399-6414, Jul. 2017. (JCR Q1)