EECS Seminar: Using Old Problems and New Applications to Understand 6G and Beyond Communications

Abstract: The projected growth in demand for wireless broadband applications is driving industry and academic research. To enable future systems, it is critical to look beyond the waveforms and signal processing schemes of today. In this talk, I will look at the impact of both AI/ML and new applications on physical layer design. I will review the use of channel output feedback in additive white Gaussian channels and discuss the surprising (and sensitive) classical results on capacity-achieving linear processing schemes. The practical shortcomings of these kinds of linear schemes will be overviewed. I will then show how an AI/ML approach can outperform the best-known schemes. In addition, I will discuss the increasingly important areas of rural communications and agriculture. I will show how this emerging use case will drive 6G and beyond waveforms.

Bio: David J. Love is the Nick Trbovich Professor of electrical and computer engineering at Purdue University. He received B.S., M.S.E. and Ph.D. degrees from UT-Austin. He is currently a senior editor for IEEE Journal on selected areas in communications and held editorial positions for IEEE Signal Processing Magazine, Communications, IEEE Trans, and Signal Processing. He holds 32 U.S. patents. His research interests include 6G and beyond wireless, MIMO communications, millimeter-wave wireless, software-defined radios and coding theory. He is a fellow of the IEEE, American Association for the Advancement of Science (AAAS) and National Academy of Inventors (NAI). His research has been recognized by the IEEE Communications Society (2016 Stephen O. Rice Prize, 2020 Fred W. Ellersick Prize and 2024 William R. Bennett Prize), IEEE Signal Processing Society (2015 SPS Best Paper Award) and IEEE VT Society (2010 Jack Neubauer Memorial Award).