Dr. Sundeep Prabhakar Chepuri

Title: Integrated Sensing and Communications: Learning, Beamforming, and Reconfigurable Intelligent Surfaces 

Time: Friday, October 27, 3:00 PM
Location: CSIP library (room 5126), 5th floor, Centergy one building

Bio: Sundeep Prabhakar Chepuri received his M.Sc. degree (cum laude) in electrical engineering and Ph.D. degree (cum laude) from the Delft University of Technology, The Netherlands, in July 2011 and January 2016, respectively. He was a Postdoctoral researcher at the Delft University of Technology, The Netherlands, a visiting researcher at University of Minnesota, USA, and at Aalto University, Finland. He has held positions at Robert Bosch, India, during 2007- 2009, and Holst Centre/imec-nl, The Netherlands, during 2010-2011. Currently, he is an Assistant Professor at the Department of ECE at the Indian Institute of Science (IISc) in Bengaluru, India.
Dr. Chepuri was a recipient of the Best Paper Awards at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2015 and at ASILOMAR 2019, and the Pratiksha Trust Young Investigator award. He was an Associate Editor of the EURASIP Journal on Advances in Signal Processing. Currently, he is an elected member of the EURASIP Technical Area Committee (TAC) on Signal Processing for Multisensor Systems, IEEE SPS Sensor Array and Multichannel Technical Committee (SAM-TC), IEEE SPS Signal Processing Theory and Methods Technical Committee (SPTM-TC), and an Associate Editor of IEEE Signal Processing Letters. His general research interest lies in the field of mathematical signal processing, statistical inference, and machine learning applied to network sciences and wireless communications.

Abstract: Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless systems, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands. Operating at these high frequencies is challenging due to the penetrating pathloss, which is so severe that the non-line-of-sight paths can be too weak to be of any practical use, preventing reliable communication or sensing. Recent years have witnessed a growing research and industrial attention in using reconfigurable intelligent surfaces (RISs) to modify the harsh propagation environment and establish reliable links for communication in Multiple-Input Multiple-Output (MIMO) systems.  In this talk, we will discuss transmit beamforming problems in ISAC and in particular to learn the beamformers using machine learning tools. Further, we will discuss how RIS can assist ISAC systems.