Event Date
While the past two decades have witnessed the successful application of multiple-input-multiple-output (MIMO) systems, the next-generation wireless systems are targeting even larger antenna arrays for higher spatial-multiplexing gain and joint communication and sensing capabilities. However, many conventional solutions lack efficiency when handling large-scale problems. In this talk we will consider large scale optimization problems arising in multi-antenna signal processing and propose low-complexity solutions for adaptive beamforming and super-resolution sparse reconstruction using fast-converging iterative methods. In addition to reducing algorithmic complexity, the proposed approaches use distributed processing techniques to overcome massive data sharing problem in digital array architectures.
Bio
Danijela Cabric is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. She received M.S. from the University of California, Los Angeles in 2001 and Ph.D. from University of California, Berkeley in 2007, both in Electrical Engineering. In 2008, she joined UCLA as an Assistant Professor, where she heads Cognitive Reconfigurable Embedded Systems lab. Her current research projects include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. Prof. Cabric was a recipient of the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012 and the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012, and Qualcomm Faculty Award in 2020 and 2021. Prof. Cabric is an IEEE Fellow.