Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning X Chen, H Zhang, C Wu, S Mao, Y Ji, M Bennis IEEE Internet of Things Journal 6 (3), 4005-4018, 2018 | 642 | 2018 |
Edge computing in 5G: A review N Hassan, KLA Yau, C Wu IEEE Access 7, 127276-127289, 2019 | 383 | 2019 |
AVE: Autonomous vehicular edge computing framework with ACO-based scheduling J Feng, Z Liu, C Wu, Y Ji IEEE Transactions on Vehicular Technology 66 (12), 10660-10675, 2017 | 332 | 2017 |
Federated learning for vehicular internet of things: Recent advances and open issues Z Du, C Wu, T Yoshinaga, KLA Yau, Y Ji, J Li IEEE Open Journal of the Computer Society 1, 45-61, 2020 | 314 | 2020 |
QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA X Liu, XB Zhai, W Lu, C Wu IEEE Transactions on Industrial Informatics 17 (3), 2052-2061, 2019 | 243 | 2019 |
IRS-assisted secure UAV transmission via joint trajectory and beamforming design X Pang, N Zhao, J Tang, C Wu, D Niyato, KK Wong IEEE Transactions on Communications 70 (2), 1140-1152, 2021 | 204 | 2021 |
Mobile edge computing for the internet of vehicles: Offloading framework and job scheduling J Feng, Z Liu, C Wu, Y Ji IEEE vehicular technology magazine 14 (1), 28-36, 2018 | 177 | 2018 |
Flexible, portable, and practicable solution for routing in VANETs: A fuzzy constraint Q-learning approach C Wu, S Ohzahata, T Kato IEEE Transactions on Vehicular Technology 62 (9), 4251-4263, 2013 | 171 | 2013 |
Performance optimization in mobile-edge computing via deep reinforcement learning X Chen, H Zhang, C Wu, S Mao, Y Ji, M Bennis 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), 1-6, 2018 | 165 | 2018 |
Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective X Chen, C Wu, T Chen, H Zhang, Z Liu, Y Zhang, M Bennis IEEE Transactions on wireless communications 19 (4), 2268-2281, 2020 | 161 | 2020 |
Collaborative learning of communication routes in edge-enabled multi-access vehicular environment C Wu, Z Liu, F Liu, T Yoshinaga, Y Ji, J Li IEEE Transactions on Cognitive Communications and Networking 6 (4), 1155-1165, 2020 | 134 | 2020 |
Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach X Chen, Z Zhao, C Wu, M Bennis, H Liu, Y Ji, H Zhang IEEE Journal on Selected Areas in Communications 37 (10), 2377-2392, 2019 | 129 | 2019 |
A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks JA Ansere, G Han, H Wang, C Choi, C Wu IEEE Internet of Things Journal 6 (4), 6748-6759, 2019 | 128 | 2019 |
Computation offloading in beyond 5G networks: A distributed learning framework and applications X Chen, C Wu, Z Liu, N Zhang, Y Ji IEEE Wireless Communications 28 (2), 56-62, 2021 | 120 | 2021 |
Spatial intelligence toward trustworthy vehicular IoT C Wu, Z Liu, D Zhang, T Yoshinaga, Y Ji IEEE Communications Magazine 56 (10), 22-27, 2018 | 119 | 2018 |
VANET broadcast protocol based on fuzzy logic and lightweight retransmission mechanism C Wu, S Ohzahata, T Kato IEICE transactions on communications 95 (2), 415-425, 2012 | 110 | 2012 |
Decentralized trust evaluation in vehicular Internet of Things S Guleng, C Wu, X Chen, X Wang, T Yoshinaga, Y Ji IEEE Access 7, 15980-15988, 2019 | 107 | 2019 |
Reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion for industrial big spectrum data X Liu, C Sun, M Zhou, C Wu, B Peng, P Li IEEE Transactions on Industrial Informatics 17 (5), 3391-3400, 2020 | 97 | 2020 |
A reinforcement learning-based data storage scheme for vehicular ad hoc networks C Wu, T Yoshinaga, Y Ji, T Murase, Y Zhang IEEE Transactions on Vehicular Technology 66 (7), 6336-6348, 2016 | 97 | 2016 |
Big-data-based intelligent spectrum sensing for heterogeneous spectrum communications in 5G X Liu, Q Sun, W Lu, C Wu, H Ding IEEE Wireless Communications 27 (5), 67-73, 2020 | 95 | 2020 |