Grand Challenges in Coexistence of Sensing and Communications: How Learning Can be a Path Forward?

Abstract:

As next-generation communication services and satellite systems utilize more frequency bands, the potential interference risks of active communications to radars and passive sensing systems used for environmental and atmospheric sensing are increasing. It is imperative to develop efficient methods to facilitate coexistence of both communication and active or passive sensing systems. Novel signal processing and machine learning approaches have potential to play a significant role towards the goal of efficient spectrum sharing. This talk will cover several important challenges within the area and will provide recent results from my research group in terms of creating an INtegrated Testbed Ensuring Resilient Active/Passive CoexisTence (INTERACT) and data-driven end-to-end learning-based approaches.

Biography:

Ali Cafer Gurbuz received a BS degree from Bilkent University, Ankara, Turkey, in 2003, in electrical engineering, and a MS and PhD degrees from Georgia Institute of Technology, Atlanta, GA, USA, in 2005 and 2008, both in electrical and computer engineering. He held faculty positions at both assistant and associate professor levels between 2009 and 2016 in Turkey, where he pursued an active research program on the development of sparse signal representations, compressive sensing theory and machine learning with applications to radar, and remote sensing and wireless communications. Currently, Gurbuz is an assistant professor at Mississippi State University, Department of Electrical and Computer Engineering, where he is the director of Information Processing and Sensing (IMPRESS) Lab. He is the author of over 50 journal and 100 conference papers. He is the recipient of NSF CAREER Award in 2021, Turkish Academy of Sciences Best Young Scholar Award and several best paper awards. Gurbuz is a senior member of IEEE.