Mao, Bomin

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

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

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