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Colloquium - Dai

Subspace Pursuit for Compressive Sensing and Wireless Communications
Wei Dai
University of Illinois at Urbana-Champaign
10/24/2008
2:00pm-3:00pm

Compressive sensing (CS) has recently received significant attention in the statistics, signal processing, communications, information theory research communities, due to its large potential for practical applications. In this talk, we will first give a brief tutorial on CS and then propose a new method for signal reconstruction in CS, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques, and reconstruction accuracy of the same order as that of linear programming optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean squared error of the reconstruction is upper bounded by constant multiples of the measurement and signal perturbation energies.

The idea of compressive sensing can be applied to wireless communications. In particular, compressive sensing allows for a novel approach to address multiuser communication systems with high-mobility and channel uncertainty. This approach may lead to significant performance improvement of existing non-asymptotic interference cancellation methods. The talk is based on a joint work with Prof. Olgica Milenkovic at the University of Illinois at Urbana-Champaign.

Dr. Wei Dai received his PhD and MS degree in Electrical and Computer Engineering from the University of Colorado Boulder in 2007 and 2004 respectively. He is currently a Postdoctoral Research Associate at the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests include information theory, communication theory, compressive sensing, bioinformatics, and random matrix theory.

Sponsored by the Interdisciplinary Telecommunications Program.

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University of Colorado Boulder
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