Wed. Apr 28th, 2:00pm. EEB 248
Abstract: Graph-based codes are fast becoming channel codes of choice for most high-speed communication systems. Attractive features of these codes are that they are capacity-approaching in the limit of large blocklengths and they can be decoded using message passing algorithms. Due to the physical limitations of practical systems, deployed codes are necessarily of finite-length and the decoding messages are necessarily quantized. This creates a significant disconnect between what is theoretically achievable and what is practically realizable. I will discuss a theoretical framework that is cognizant of such practical constraints. By taking into account algebraic and combinatorial properties of objects characterizing dominant decoding failures, which are viewed as rare events for high-performance communication systems, we can gain a better understanding of the limitations of practical systems. I will demonstrate how the proposed framework leads to a systematic improvement of the communication system as a whole. Specific aspects of the system design I will discuss are: improved practical decoding algorithms, better structured code designs and more efficient sampling strategies for performance prediction.
In a broader realm of future high-speed complex systems, I will also discuss the emerging applications in the domain of nano-scale circuits. I will present an approach based on the information theoretic ideas of rare events coupled with the suitable fast statistical algorithms, that can be successfully applied for evaluating the yield, and in turn a better design, of nano-scale circuit systems.
Lara Dolecek is an Assistant Professor with the Electrical Engineering Department at the University of California, Los Angeles (UCLA). She holds a B.S. (with honors), M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences, as well as an M.A. degree in Statistics, all from the University of California, Berkeley. For her dissertation she received the 2007 David J. Sakrison Memorial Prize for the most outstanding doctoral research in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Prior to joining UCLA, she spent two years as a postdoctoral researcher with the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Her research interests span information and probability theory, graphical models, statistical algorithms, and computational methods, with applications to complex systems for data processing, communication, and storage.
Host: Alex Dimakis, dimakis [at] usc.edu
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