Kumar Vijay Mishra of George Washington University and the IEEE Computer Society

The millimeter-wave (mm-Wave) massive MIMO communications employ hybrid analog-digital beamforming architectures to reduce the cost-power-size-hardware overheads. Lately, there is also a gradual push to move from the millimeter-wave (mmWave) to Terahertz (THz) frequencies for short-range communications and radar applications to exploit very wide THz bandwidths. The design of the hybrid beamforming techniques requires solving difficult nonconvex optimization problems that involve a common performance metric as a cost function and a number of constraints related to the employed communications/radar regime and the adopted architecture of the hybrid systems. There is no standard methodology for solving such problems and usually, the derivation of an efficient solution is a very challenging task. Since optimization-based approaches suffer from high computational complexity and their performance strongly relies on the perfect channel condition, we introduce deep learning (DL) techniques that provide robust performance while designing a hybrid beamformer. These methods offer advantages such as low computational complexity and the ability to extrapolate new features from a limited set of features contained in a training set. In this talk, the audience will learn about applying DL to various aspects of hybrid beamforming including channel estimation, antenna selection, wideband beamforming, knowledge transfer across various geometries, and spatial modulation in both communications and radar.

Biography:

Dr. Kumar Vijay Mishra (IEEE S’08-M’15-SM’18) obtained a Ph.D. in electrical engineering and M.S. in mathematics from The University of Iowa in 2015, and M.S. in electrical engineering from Colorado State University in 2012, while working on NASA’s Global Precipitation Mission Ground Validation (GPM-GV) weather radars. Dr. Mishra received his B. Tech. summa cum laude (Gold Medal, Honors) in electronics and communication engineering from the National Institute of Technology, Hamirpur (NITH), India in 2003. Dr. Mishra is currently Senior Fellow at the United States Army Research Laboratory (ARL), Adelphi; Technical Adviser to Singapore-based automotive radar start-up Hertzwell; and honorary Research Fellow at SnT – Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg.

Dr. Mishra is the recipient of U. S. National Academies Harry Diamond Distinguished Fellowship (2018-2021), Royal Meteorological Society Quarterly Journal Editor’s Prize (2017), Viterbi Postdoctoral Fellowship (2015, 2016), Lady Davis Postdoctoral Fellowship (2017), and DRDO LRDE Scientist of the Year Award (2006). Dr. Mishra is Vice-Chair (2021-present) of the IEEE Synthetic Aperture Standards Committee of the IEEE Signal Processing Society. Since 2020, he has been Associate Editor of IEEE Transactions on Aerospace and Electronic Systems. Dr. Mishra is Vice Chair (2021-2023) and Chair-designate (2023-2026) of International Union of Radio Science (URSI) Commission C. Dr. Mishra is the co-editor of three upcoming books on radar: Signal Processing for Joint Radar-Communications (Wiley-IEEE Press), Next-Generation Cognitive Radar Systems (IET Press), and Advances in Weather Radar Volumes 1, 2 & 3 (IET Press). Dr. Mishra’s research interests include radar systems, signal processing, remote sensing, and electromagnetics.