WiOpt 2024 Workshop on
WiOpt 2024 Workshop on
Embedded Files
Integrated Sensing and Communications (ISAC)
Integrated Sensing and Communications (ISAC)
October 21-24, 2024, Seoul, Korea
October 21-24, 2024, Seoul, Korea
Keynote Speaker
Ahmed Alkhateeb
Assistant Professor
Arizona State University, US
Education
Ph.D. Electrical Engineering, University of Texas-Austin 2016
M.S. Electrical Engineering, Cairo University, Egypt 2012
B.S. Electrical Engineering (with distinction and honors), Cairo University, Egypt 2008
Ahmed Alkhateeb received his B.S. and M.S. degrees in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical Engineering from The University of Texas at Austin, USA, in August 2016. In Sept. 2016- Dec. 2017, he was a Wireless Communications Researcher at the Connectivity Lab, Facebook, in Menlo Park, CA. He joined Arizona State University (ASU) in Spring 2018, where he is currently an Assistant Professor in the School of Electrical, Computer, and Energy Engineering. His research interests are in the broad areas of wireless communications, communication theory, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award in 2021.
Talk Title
The Interplay Between Multi-Modal Sensing and Communications: Machine Learning Roles
Abstract
Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several signal processing, network management, and hardware design challenges. In this talk, I will present a few directions where machine learning can play a key role in enabling the efficient integration of multi-modal sensing and communication. I will also highlight certain considerations and practical aspects when leveraging machine learning in these systems. Finally, I will present datasets and platforms that enable the research and development in the interplay between machine learning, multi-modal sensing, communication, and electromagnetic digital twins.
Page updated
Google Sites
Report abuse