Reliable Low Latency Communications for Connected Autonomy: Experienced Deep Learning and Control
Walid Saad – Virginia Tech
Abstract: In this talk, we provide an overview on the frontier of research in the area of ultra reliable low latency communications (URLLC) for connected autonomy. In particular, we first introduce a novel framework, dubbed experienced deep learning, that combines deep reinforcement learning with generative adversarial networks (GANs) to enable model-free URLLC under limited data availability and without requiring any knowledge or assumptions on the delay models of the wireless users. This framework is particularly suitable to enable reliable and low latency connectivity for connected autonomy applications whose performance is highly sensitive to the dynamics of the wireless network environment. We show how the proposed framework can intelligently optimize wireless resources while balancing the tradeoff between reliability, latency, and rate. This approach presents a major departure from prior URLLC approaches that often ignore the rate requirements of the users and rely on historic data or on unrealistic delay modeling assumptions. Then, we turn our attention to the problem of joint communications and control for autonomous connected vehicles. In this area, we introduce a new cyber-physical approach for characterizing the wireless reliability of an autonomous vehicle system while being explicitly cognizant of its control system requirements. After characterizing reliability, we show how one can optimize the operation of the autonomous vehicle system while jointly taking into account the delay of the vehicular network and the stability of the control system The synergies between URLLC and control system designs are then discussed. We conclude the talk with an overview on future opportunities in these exciting areas.
Biography: Walid Saad received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is also the recipient of the NSF CAREER award in 2013 and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of nine conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, and IEEE ICC in 2020. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and of the 2019 IEEE ComSoc Communication Theory Technical Committee. He was also a co-author of the 2019 IEEE Communications Society Young Author Best Paper. He received the Dean’s award for Research Excellence from Virginia Tech in 2019. He currently serves as an editor for most major IEEE Transactions.
Hosts: Bhaskar Krishnamachari and Pierluigi Nuzzo