Spatial Deep Learning for Wireless Scheduling

Speaker:   Prof. Wei Yu

Time:       15:30-16:30, Aug. 23

Location:  SIST 1C 502

Host:       Prof. Yuanming Shi

Abstract:

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. In this talk, we first propose a novel fractional programming method to solve this problem, then point out that the traditional optimization approach of first  estimating all the interfering channel strengths then optimizing the scheduling based on the model is not always practical, because channel estimation is resource intensive, especially in dense networks. To address this issue, we investigate the possibility of using a deep learning approach to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers. This is accomplished by using locally optimal schedules generated using fractional programming for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives good performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Further, we propose a novel approach of utilizing the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links to provide fair scheduling across the network, thereby showing the promise of using deep learning to solve discrete optimization problems in wireless networking.

Bio:

Wei Yu received the B.A.Sc. degree in Computer Engineering and Mathematics from the University of Waterloo, Waterloo, Ontario, Canada in 1997 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, in 1998 and 2002, respectively. He is now Professor and holds a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications in the Electrical and Computer Engineering Department at the University of Toronto, Canada.  Prof. Wei Yu currently serves on the IEEE Information Theory Society Board of Governors. He was an IEEE Communications Society Distinguished Lecturer (2015-16), and currently serves as an Area Editor for the IEEE Transactions on Wireless Communications. He is currently the Chair of the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society. Prof. Wei Yu received the IEEE Signal Processing Society Best Paper Award in 2017 and 2008, the Journal of Communications and Networks Best Paper Award in 2017, an E.W.R. Steacie Memorial Fellowship in 2015, and an IEEE Communications Society Best Tutorial Paper Award in 2015. Prof. Wei Yu is recognized as a Highly Cited Researcher. He is a Fellow of IEEE, a Fellow of Canadian Academy of Engineering, and a member of the Royal Society of Canada's College of New Scholars, Artists and Scientists.

SIST-Seminar 18195