Lecture Topic:

Reinforcement Learning for Resource Management in Space-Air-Ground (SAG) Integrated Vehicular Networks

lecturer:

Shen Xuemin (Professor, Fellow of the Canadian Academy of Engineering)

Time:

September 28, 2019 (Saturday) 14:30-16:00

Place:

Conference Room 205, Information Science Laboratory Building, Zhongguancun Campus

Organizer:

Graduate School, School of Information and Electronics

【Introduction to the lecturer】

Xuemin (Sherman) Shen is a University Professor, and Associate Chair for Graduate Study, Department of Electrical and Computer Engineering, University of Waterloo, Canada. Dr. Shen's research focuses on wireless resource management, wireless network security, smart grid and vehicular ad hoc and sensor networks. He is the Editor-in-Chief of IEEE IoT J. He serves as the General Chair for Mobihoc'15, the Technical Program Committee Chair for IEEE Globecom'16, IEEE Infocom'14, IEEE VTC'10, the Symposia Chair for IEEE ICC'10, the Technical Program Committee Chair for IEEE Globecom'07, the Chair for IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is an elected IEEE ComSoc Vice President - Publications, the chair of IEEE ComSoc Distinguish Lecturer selection committee, and a member of IEEE ComSoc Fellow evaluation committee. Dr. Shen received the Excellent Graduate Supervision Award in 2006, and the Premier's Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an IEEE Fellow, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, and a Distinguished Lecturer of IEEE Vehicular Technology Society and Communications Society.

【Lecture Information】

Space-Air-Ground integrated Vehicular Network (SAGVN) is a prominent paradigm to provide an extremely versatile vehicular network that can simultaneously guarantee ultra-reliability low-latency communications (URLLC) and deliver high-bandwidth traffic anywhere, any environment condition, and any event at anytime. However, it is challenging to manage and allocate the terrestrial network, aerial network (UAV), and space (satellite) resources simultaneously and efficiently, as they have heterogeneous access features in terms of delay, throughput, and coverage range. In addition, high vehicle mobility and real-time decision requirement further render the problem intractable. In this talk, we advocate the usage of reinforcement learning for resource management in SAGVN, which can enable model-free and fast decision makings for adaptive access control, on-demand UAV deployment, and UAV trajectory design. We will also show the detail development of our SAG simulator and some demos.