Dola Saha of University at Albany, SUNY

The growing need for spectrum to support the next generation communication networks, increasingly generates unwanted Radio Frequency Interference (RFI) in the protected frequency bands allocated for passive scientific users, like Radio Astronomy Services (RAS) and Remote Sensing. At the same time, it is increasingly necessary for RAS to operate in frequencies outside these allocations to accommodate for red-shift of spectral lines and to maximize the fidelity of the observations by sensing over a wider bandwidth. Thus far, interference from communication networks have always been dealt with complete excision of RFI contaminated frequencies that lowers the overall sensitivity of the telescope and results in permanent and persistent loss of astronomical data. This necessitates stringent interference mitigation techniques to continue this discovery based scientific research in presence of RFI. This talk presents two approaches to cancel RFI for RAS. The first part of the talk will present an innovative method to decompose the astronomical and the RFI signals from nearby cellular networks in order to isolate, characterize and cancel the RFI by Deep Learning models. It is a bold step towards inter-technology interference cancellation through active bidirectional collaboration. The second part will present a new paradigm of mitigating RFI from mobile sources, like aircrafts, UAVs and LEO satellites by accurately detecting, locating and canceling the RFI by employing Reconfigurable Intelligent Surfaces (RIS). Initial results with real astronomical signals captured at Owens Valley Radio Observatory (OVRO) will be presented to show the efficacy of the two systems. These enhancements have the potential to transcend Radio Telescopes from passive receivers to cognitive and intelligent instruments, greatly increasing its contribution to future astronomical explorations.

Biography:

Dola Saha is an Associate Professor in the Department of Electrical & Computer Engineering at University at Albany, SUNY. Prior to that, she was a Research Assistant Professor in WINLAB at Rutgers University and a Researcher in the Mobile Communications and Networking group at NEC Laboratories America. Dr. Saha has been a faculty fellow at Jet Propulsion Laboratory, NASA and a visiting faculty at the Air Force Research Laboratory in Rome, NY. Currently, she serves as the Associate Editor for IEEE Transactions on Cognitive Communications and Networking and IEEE Transactions on Machine Learning and Communications. Dr. Saha received her Masters and Doctorate degrees in Computer Science from the University of Colorado Boulder and is a recipient of the Google Anita Borg Scholarship. Her research interests lie at the crossroads of Machine Learning for Wireless Communication, Wireless Signal Processing and Wireless Security with focus on systems design and practical evaluation.