| 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 | 896 | 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 | 444 | 2016 |
| 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 | 365 | 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 | 361 | 2019 |
| 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 | 359 | 2020 |
| 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 | 280 | 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 | 258 | 2018 |
| AI-based joint optimization of QoS and security for 6G energy harvesting Internet of Things B Mao, Y Kawamoto, N Kato IEEE Internet of Things Journal 7 (8), 7032-7042, 2020 | 178 | 2020 |
| A deep-learning-based radio resource assignment technique for 5G ultra dense networks Y Zhou, ZM Fadlullah, B Mao, N Kato IEEE Network 32 (6), 28-34, 2018 | 172 | 2018 |
| AI models for green communications towards 6G B Mao, F Tang, Y Kawamoto, N Kato IEEE Communications Surveys & Tutorials 24 (1), 210-247, 2021 | 154 | 2021 |
| Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges F Tang, B Mao, N Kato, G Gui IEEE Communications Surveys & Tutorials 23 (3), 2027-2057, 2021 | 144 | 2021 |
| Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption F Tang, B Mao, Y Kawamoto, N Kato IEEE Communications Surveys & Tutorials 23 (3), 1578-1598, 2021 | 135 | 2021 |
| 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 | 129 | 2018 |
| Optimizing computation offloading in satellite-UAV-served 6G IoT: A deep learning approach B Mao, F Tang, Y Kawamoto, N Kato Ieee Network 35 (4), 102-108, 2021 | 127 | 2021 |
| 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 | 125 | 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 | 106 | 2019 |
| 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 | 88 | 2018 |
| Security and privacy on 6g network edge: A survey B Mao, J Liu, Y Wu, N Kato IEEE communications surveys & tutorials, 2023 | 67 | 2023 |
| Intelligent reflecting surface-aided vehicular networks toward 6G: Vision, proposal, and future directions Y Zhu, B Mao, Y Kawamoto, N Kato IEEE Vehicular Technology Magazine 16 (4), 48-56, 2021 | 55 | 2021 |
| A dynamic task scheduling strategy for multi-access edge computing in IRS-aided vehicular networks Y Zhu, B Mao, N Kato IEEE Transactions on Emerging Topics in Computing 10 (4), 1761-1771, 2022 | 50 | 2022 |
Nei Kato(IEEE Fellow, IEICE Fellow)Professor and Dean, Graduate School of Information Sciences, Tohoku University, Japan在 it.is.tohoku.ac.jp 的电子邮件经过验证
