On Compressive Sensing in Coding Problems: A Rigorous Approach
Abstract
We take an information theoretic perspective on a classical sparse-sampling noisy linear model and present an analytical expression for the mutual information, which plays central role in a variety of communications/processing problems. Such an expression was addressed previously either by bounds, by simulations and by the (non-rigorous) replica method. The expression of the mutual information is based on techniques used in [1], addressing the minimum mean square error (MMSE) analysis. Using these expressions, we study specifically a variety of sparse linear communications models which include coding in different settings, accounting also for multiple access channels and different wiretap problems. For those, we provide single-letter expressions and derive achievable rates, capturing the communications/processing features of these timely models.
Cite
@article{arxiv.1403.5874,
title = {On Compressive Sensing in Coding Problems: A Rigorous Approach},
author = {Wasim Huleihel and Neri Merhav and Shlomo Shamai},
journal= {arXiv preprint arXiv:1403.5874},
year = {2014}
}