CAREER: Information Theory and Iterative Decoding for Channels With Memory

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

The main thrust of this research is the development of innovative new techniques for the transmission and storage of digital information. The storage and transmission of digital information is now so ubiquitous in our society, that improving the state of the art in this area has a ripple effect that can enable advances in many other fields. As this technology is pushed to its limits, increasingly complex models are required to represent communication systems accurately. To this end, this research focuses on advanced system models whose properties depend on previous inputs and outputs from the system. Hard disk drives are a good representative of these complicated systems and this research should help improve the capacity and reliability of future magnetic storage systems. The unifying theme of this research theme is a class of communication channels (with memory) known as finite-state channels (FSCs). FSCs are a very general class of channels that are used to model complex channels including correlated fading channels, inter-symbol interference channels, and magnetic recording channels. The first part of this research considers the practical computation of information-theoretic quantities, such as the capacity and the error exponent, that are important for the design of magnetic storage devices with very low failure rates. The second part involves the joint iterative decoding for FSCs and low-density parity-check (LDPC) codes. In particular, this work considers a pseudo-codeword analysis for joint decoding that can be used to optimize codes for finite block lengths. The third part studies the rate-distortion (RD) problem for finite-state sources; characterizing this RD curve for large distortions has long been an open problem in information theory.