Event

Title: Blind Carbon Copy on Dirty Paper: Seamless Spectrum Underlay via Canonical Correlation Analysis 

 

Livestream link: https://go.umd.edu/nikos

 

Abstract: The unprecedented growth in wireless Internet-of-Things and 5G / WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection - which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. In this talk, I will present a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary's interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Remarkably, this works with unknown time-varying channels, and digital or analog modulation. The approach is immune to carrier frequency offset, and it also provides means for accurate synchronization of the secondary user even at very low SINR. In this context, repetition coding can be viewed as providing means for signal (as opposed to interference) alignment. CCA is a popular tool in statistics and machine learning, but one that remains poorly understood from the algebraic point of view. I will therefore start with a brief overview of recent results obtained by my group on CCA and its identifiability properties, which prompted us to consider this particular application. We will finish with a discussion of laboratory experiments using a software radio testbed, confirming that for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible at signal to interference plus noise ratio (SINR) in the range of -20 to -40 dB.  

 

Bio: N. Sidiropoulos is the Louis T. Rader Professor of Electrical and Computer Engineering at the University of Virginia. He earned his Ph.D. in Electrical Engineering from the University of Maryland–College Park, in 1992. He has served on the faculty of the University of Minnesota, and the Technical University of Crete, Greece. His research interests are in signal processing, communications, optimization, tensor decomposition, and factor analysis, with applications in machine learning and communications. He received the NSF/CAREER award in 1998, the IEEE Signal Processing Society (SPS) Best Paper Award in 2001, 2007, and 2011, and his students received four IEEE SPS conference best paper awards. Sidiropoulos has authored a Google Classic Paper in Signal Processing (on multicast beamforming), and his tutorial on tensor decomposition is ranked #1 in Google Scholar metrics for IEEE Transactions in Signal Processing (TSP), and tops the charts of the most popular / most frequently accessed TSP papers in IEEExplore. He served as IEEE SPS Distinguished Lecturer (2008-2009), Vice President of IEEE SPS (2017-2019), and chair of the IEEE Fellow evaluation committee of SPS (2020-2021). He received the 2010 IEEE Signal Processing Society Meritorious Service Award, and the 2013 Distinguished Alumni Award from the ECE Department of the University of Maryland. He is a Fellow of IEEE (2009) and a Fellow of EURASIP (2014). More information at http://www.ece.virginia.edu/~nds5j/ and https://scholar.google.com/citations?user=ZOkfkFMAAAAJ&hl=en