Fadlullah, Zubair Md.

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

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

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