Kato, Nei

Space-Air-Ground Integrated Network: A Survey

Liu, J., Shi, Y., Fadlullah, Z. M., & Kato, N. (2018) · IEEE Communications Surveys & Tutorials

Satellite Communication SystemsUAV Applications and Optimization

Cited by 1,281

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials

Software-Defined Networks and 5GNetwork Traffic and Congestion Control

Cited by 810

6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence

Gui, G., Liu, M., Tang, F., Kato, N., & Adachi, F. (2020) · IEEE Wireless Communications

Advanced Wireless Communication TechnologiesSoftware-Defined Networks and 5G

Cited by 733

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE

Vehicular Ad Hoc Networks (VANETs)Advanced Wireless Communication Technologies

Cited by 620

A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Verma, S., Kawamoto, Y., Fadlullah, Z. M., Nishiyama, H., & Kato, N. (2017) · IEEE Communications Surveys & Tutorials

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 422

The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Wireless Communications

Software-Defined Networks and 5GNetwork Traffic and Congestion Control

Cited by 400

On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

Tang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2018) · IEEE Wireless Communications

Software-Defined Networks and 5GNetwork Traffic and Congestion Control

Cited by 247

AI-Based Joint Optimization of QoS and Security for 6G Energy Harvesting Internet of Things

Mao, B., Kawamoto, Y., & Kato, N. (2020) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingEnergy Harvesting in Wireless Networks

Cited by 228

When Machine Learning Meets Privacy in 6G: A Survey

Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 211

Device-to-device communications for enhancing quality of experience in software defined multi-tier LTE-A networks

Liu, J., Zhang, S., Kato, N., Ujikawa, H., & Suzuki, K. (2015) · IEEE Network

Software-Defined Networks and 5GAdvanced MIMO Systems Optimization

Cited by 184

Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective

Zhao, L., Wang, J., Liu, J., & Kato, N. (2019) · IEEE Communications Magazine

Software-Defined Networks and 5GIoT and Edge/Fog Computing

Cited by 126

On a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT

Tang, F., Mao, B., Fadlullah, Z. M., & Kato, N. (2018) · IEEE Communications Magazine

Software-Defined Networks and 5GIoT and Edge/Fog Computing

Cited by 123

Distributed Q-Learning Aided Uplink Grant-Free NOMA for Massive Machine-Type Communications

Liu, J., Shi, Z., Zhang, S., & Kato, N. (2021) · IEEE Journal on Selected Areas in Communications

IoT Networks and ProtocolsAdvanced Wireless Communication Technologies

Cited by 53

A PSO model with VM migration and transmission power control for low Service Delay in the multiple cloudlets ECC scenario

Rodrigues, T. G., Suto, K., Nishiyama, H., & Kato, N. (2017) · 2017 IEEE International Conference on Communications (ICC)

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 26