2018 IEEE VTS Distinguished Lecture Tour: Resource Allocation in Vehicular Communication

Share

This talk will address resource allocation in vehicular communications. Different from traditional resource allocation, strong dynamics caused by high mobility in the vehicular environments poses a serious obstacle to the acquisition of high-quality channel state information (CSI). To deal with the issue, we investigate the delay impacts of periodic CSI feedback and develop efficient graph-based centralized resource management schemes to meet the diverse quality-of-service (QoS) requirements in vehicular networks. To further reduce signaling overhead, we take advantage of recent advances in reinforcement learning (RL) and develop an effective distributed resource allocation scheme. We will show that the demanding latency and reliability requirements of vehicular communications, which are hard to model and analyze using traditional methods, can be explicitly accounted for in the proposed deep RL framework.



  Date and Time

  Location

  Hosts

  Registration



  • Linköping University
  • Campus Valla
  • Linköping, Ostergotlands lan
  • Sweden 581 83
  • Building: B-building,
  • Room Number: Systemet, floor 2, entrance 27
  • Click here for Map

Staticmap?size=250x200&sensor=false&zoom=14&markers=58.3962144%2c15
  • Co-sponsored by Linköping University


  Speakers

Prof. Geoffrey Li

Prof. Geoffrey Li

Topic:

Resource Allocation in Vehicular Communications

This talk will address resource allocation in vehicular communications. Different from traditional resource allocation, strong dynamics caused by high mobility in the vehicular environments poses a serious obstacle to the acquisition of high-quality channel state information (CSI). To deal with the issue, we investigate the delay impacts of periodic CSI feedback and develop efficient graph-based centralized resource management schemes to meet the diverse quality-of-service (QoS) requirements in vehicular networks. To further reduce signaling overhead, we take advantage of recent advances in reinforcement learning (RL) and develop an effective distributed resource allocation scheme. We will show that the demanding latency and reliability requirements of vehicular communications, which are hard to model and analyze using traditional methods, can be explicitly accounted for in the proposed deep RL framework.

Biography:

Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He is also holding a Cheung Kong Scholar title at the University of Electronic Science and Technology of China since 2006. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing for wireless communications. Recently, he focuses on intelligent processing for communication networks. In these areas, he has published over 200 papers on referred journals in addition to over 40 granted patents and many conference papers, with over 30,000 citations. He has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters (almost every year). He has been an IEEE Fellow since 2006. He received the 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, the 2013 IEEE VTS James Evans Avant Garde Award, the 2014 IEEE VTS Jack Neubauer Memorial Award, the 2017 IEEE ComSoc Award for Advances in Communication, and the 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.

Email:

Address:Georgia Institute of Technology, , Atlanta, Georgia, United States