Rateless Coding at the Physical Layer Made Surprisingly Simple

Professor Gregory W. Wornell
Professor, Massachusetts Institute of Technology
Given on: December 6, 2012

Abstract

In a growing number of applications, we need to reliably communicate at the highest possible rate over highly dynamic and unpredictable channels. Rateless coding is a natural solution to such problems, and can be viewed as efficient joint design of the physical and link layers in the network protocol stack. Moreover, information theoretic analysis establishes that the desired capacity-achieving rateless codes exist. As such, in recent years there has been growing interest in the development of practical (i.e., low-complexity) codes for approaching these fundamental limits. While there have been good low-complexity capacity-approaching codes for implementation at the application layer, such codes give up much performance in wireless applications by not directly controlling physical layer resources. In this talk, I describe a surprisingly simple framework for transforming standard good off-the-shelf codes for the traditional additive white Gaussian noise (AWGN) channel into good rateless codes. This framework is both capacity- and complexity-preserving. I will describe two variants of this framework: one based on a layered architecture and successive interference cancellation receivers, and the other based on novel super-Nyquist signaling and decision feedback equalization. In addition to developing their performance characteristics and discussing their implementation, I will comment on the role they may ultimately play in the development of efficient interference-resilient networks.

Based on joint work with Uri Erez and Mitchell D. Trott.

Biography

Greg Wornell has been on the MIT faculty since 1991, where he is Professor in the department of Electrical Engineering and Computer Science. He did his graduate work also at MIT in EECS, and his undergraduate work at the University of British Columbia. His research interests span a variety of aspects of signal processing, information theory, digital communication, and statistical inference, and include algorithms and architectures for wireless networks, multimedia data, and imaging systems, among other applications.