URLLC resource slicing and scheduling for trustworthy 6G vehicular services: A federated reinforcement learning approach
Tài liệu tham khảo
Chen, 2020, Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed, IEEE Wirel. Commun., 27, 218, 10.1109/MWC.001.1900333
He, 2020, Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT, Physical Communication, 43, 101181, 10.1016/j.phycom.2020.101181
Huo, 2019, Enabling multi-functional 5G and beyond user equipment: A survey and tutorial, IEEE Access, 7, 116975, 10.1109/ACCESS.2019.2936291
Sekaran, 2020, Survival study on blockchain based 6G-enabled mobile edge computation for IoT automation, IEEE Access, 8, 143453, 10.1109/ACCESS.2020.3013946
Giordani, 2021, Non-terrestrial networks in the 6G era: Challenges and opportunities, IEEE Netw., 35, 244, 10.1109/MNET.011.2000493
Samaniego, 2018, Zero-trust hierarchical management in IoT, 88
Gilman, 2017
Tang, 2021, Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks, Physical Communication, 101381, 10.1016/j.phycom.2021.101381
Lim, 2020, Federated learning in mobile edge networks: A comprehensive survey, IEEE Commun. Surv. Tutor., 22, 2031, 10.1109/COMST.2020.2986024
Nguyen, 2020, Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications, IEEE Trans. Cybern., 50, 3826, 10.1109/TCYB.2020.2977374
Luong, 2019, Applications of deep reinforcement learning in communications and networking: A survey, IEEE Commun. Surv. Tutor., 21, 3133, 10.1109/COMST.2019.2916583
Zhang, 2020, Deep-reinforcement-learning-based mode selection and resource allocation for cellular V2X communications, IEEE Internet Things J., 7, 6380, 10.1109/JIOT.2019.2962715
Wang, 2020, Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching, IEEE Internet Things J., 7, 9441, 10.1109/JIOT.2020.2986803
Yu, 2021, When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense network, IEEE Internet Things J., 8, 2238, 10.1109/JIOT.2020.3026589
Ye, 2020, Federated learning in vehicular edge computing: A selective model aggregation approach, IEEE Access, 8, 23920, 10.1109/ACCESS.2020.2968399
Kang, 2019, Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory, IEEE Internet Things J., 6, 10700, 10.1109/JIOT.2019.2940820
Kang, 2019, Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory, IEEE Trans. Veh. Technol., 68, 2906, 10.1109/TVT.2019.2894944
Huang, 2018, Software defined networking for energy harvesting Internet of Things, IEEE Internet Things J., 5, 1389, 10.1109/JIOT.2018.2799936
Noor-A-Rahim, 2019, Reliable state estimation of an unmanned aerial vehicle over a distributed wireless IoT network, IEEE Trans. Reliab., 68, 1061, 10.1109/TR.2019.2891994
Siboni, 2019, Security testbed for Internet-of-Things devices, IEEE Trans. Reliab., 68, 23, 10.1109/TR.2018.2864536
Xiong, 2019, Smart network slicing for vehicular fog-RANs, IEEE Trans. Veh. Technol., 68, 3075, 10.1109/TVT.2019.2900234
Huang, 2020
Ye, 2019, Deep reinforcement learning based resource allocation for V2V communications, IEEE Trans. Veh. Technol., 68, 3163, 10.1109/TVT.2019.2897134
Xia, 2020, A note on implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters, IEEE Transactions on Broadcasting, 66, 744, 10.1109/TBC.2020.2985592
Xia, 2019, Secure cache-aided multi-relay networks in the presence of multiple eavesdroppers, IEEE Trans. Commun., 67, 7672, 10.1109/TCOMM.2019.2935047
Manzoor, 2020, Contract-based scheduling of URLLC packets in incumbent eMBB traffic, IEEE Access, 8, 167516, 10.1109/ACCESS.2020.3023128
Popovski, 2018, 5G wireless network slicing for eMBB, URLLC, and mMTC: A communication-theoretic view, IEEE Access, 6, 55765, 10.1109/ACCESS.2018.2872781
Yang, 2020, Joint frame design and resource allocation for ultra-reliable and low-latency vehicular networks, IEEE Trans. Wireless Commun., 19, 3607, 10.1109/TWC.2020.2975576
Liu, 2018, Offloading schemes in mobile edge computing for ultra-reliable low latency communications, IEEE Access, 6, 12825, 10.1109/ACCESS.2018.2800032
Rasheed, 2020, Adaptive group-based zero knowledge proof-authentication protocol in vehicular Ad hoc networks, IEEE Trans. Intell. Transp. Syst., 21, 867, 10.1109/TITS.2019.2899321
Hammoud, 2020, AI, blockchain, and vehicular edge computing for smart and secure IoV: Challenges and directions, IEEE Internet Things Mag., 3, 68, 10.1109/IOTM.0001.1900109
Mnih, 2016, Asynchronous methods for deep reinforcement learning, 1928
Zhan, 2020, A learning-based incentive mechanism for federated learning, IEEE Internet Things J., 7, 6360, 10.1109/JIOT.2020.2967772
Hao, 2021, URLLC resource slicing and scheduling in 5G vehicular edge computing, 1