| State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems ZM Fadlullah, F Tang, B Mao, N Kato, O Akashi, T Inoue, K Mizutani IEEE Communications Surveys & Tutorials 19 (4), 2432-2455, 2017 | 645 | 2017 |
| The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective N Kato, ZM Fadlullah, B Mao, F Tang, O Akashi, T Inoue, K Mizutani IEEE wireless communications 24 (3), 146-153, 2016 | 349 | 2016 |
| Future intelligent and secure vehicular network toward 6G: Machine-learning approaches F Tang, Y Kawamoto, N Kato, J Liu Proceedings of the IEEE 108 (2), 292-307, 2019 | 286 | 2019 |
| Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning B Mao, ZM Fadlullah, F Tang, N Kato, O Akashi, T Inoue, K Mizutani IEEE Transactions on Computers 66 (11), 1946-1960, 2017 | 278 | 2017 |
| 6G: Opening new horizons for integration of comfort, security, and intelligence G Gui, M Liu, F Tang, N Kato, F Adachi IEEE Wireless Communications 27 (5), 126-132, 2020 | 275 | 2020 |
| AC-POCA: Anticoordination game based partially overlapping channels assignment in combined UAV and D2D-based networks F Tang, ZM Fadlullah, N Kato, F Ono, R Miura IEEE Transactions on Vehicular Technology 67 (2), 1672-1683, 2017 | 239 | 2017 |
| Optimizing space-air-ground integrated networks by artificial intelligence N Kato, ZM Fadlullah, F Tang, B Mao, S Tani, A Okamura, J Liu IEEE Wireless Communications 26 (4), 140-147, 2019 | 198 | 2019 |
| On removing routing protocol from future wireless networks: A real-time deep learning approach for intelligent traffic control F Tang, B Mao, ZM Fadlullah, N Kato, O Akashi, T Inoue, K Mizutani IEEE Wireless Communications 25 (1), 154-160, 2017 | 197 | 2017 |
| An intelligent traffic load prediction-based adaptive channel assignment algorithm in SDN-IoT: A deep learning approach F Tang, ZM Fadlullah, B Mao, N Kato IEEE Internet of Things Journal 5 (6), 5141-5154, 2018 | 178 | 2018 |
| Ten challenges in advancing machine learning technologies toward 6G N Kato, B Mao, F Tang, Y Kawamoto, J Liu IEEE Wireless Communications 27 (3), 96-103, 2020 | 151 | 2020 |
| On a novel deep-learning-based intelligent partially overlapping channel assignment in SDN-IoT F Tang, B Mao, ZM Fadlullah, N Kato IEEE Communications Magazine 56 (9), 80-86, 2018 | 94 | 2018 |
| A novel non-supervised deep-learning-based network traffic control method for software defined wireless networks B Mao, F Tang, ZM Fadlullah, N Kato, O Akashi, T Inoue, K Mizutani IEEE Wireless Communications 25 (4), 74-81, 2018 | 80 | 2018 |
| On a novel adaptive UAV-mounted cloudlet-aided recommendation system for LBSNs F Tang, ZM Fadlullah, B Mao, N Kato, F Ono, R Miura IEEE Transactions on Emerging Topics in Computing 7 (4), 565-577, 2018 | 75 | 2018 |
| An intelligent route computation approach based on real-time deep learning strategy for software defined communication systems B Mao, F Tang, ZM Fadlullah, N Kato IEEE Transactions on Emerging Topics in Computing 9 (3), 1554-1565, 2019 | 53 | 2019 |
| Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet F Tang, Y Zhou, N Kato IEEE Journal on selected areas in communications 38 (12), 2773-2782, 2020 | 51 | 2020 |
| On intelligent traffic control for large-scale heterogeneous networks: A value matrix-based deep learning approach ZM Fadlullah, F Tang, B Mao, J Liu, N Kato IEEE Communications Letters 22 (12), 2479-2482, 2018 | 38 | 2018 |
| A tensor based deep learning technique for intelligent packet routing B Mao, ZM Fadlullah, F Tang, N Kato, O Akashi, T Inoue, K Mizutani GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, 2017 | 37 | 2017 |
| Value iteration architecture based deep learning for intelligent routing exploiting heterogeneous computing platforms ZM Fadlullah, B Mao, F Tang, N Kato IEEE Transactions on Computers 68 (6), 939-950, 2018 | 31 | 2018 |
| Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges F Tang, B Mao, N Kato, G Gui IEEE Communications Surveys & Tutorials, 2021 | 27 | 2021 |
| Reinforcement learning-based radio resource control in 5G vehicular network Y Zhou, F Tang, Y Kawamoto, N Kato IEEE Wireless Communications Letters 9 (5), 611-614, 2019 | 27 | 2019 |
