Compressed Sensing for Finite-Valued Signals

TL;DR

This paper introduces a compressed sensing approach tailored for finite-valued sparse signals, leveraging prior discrete value knowledge to improve recovery efficiency and robustness over classical methods.

Contribution

It presents a novel basis pursuit method that incorporates discrete-valued priors, demonstrating earlier phase transitions and reduced measurement requirements for binary and ternary signals.

Findings

Earlier phase transition compared to classical basis pursuit

At most N/2 measurements for binary signals

At most 3N/4 measurements for ternary signals

Abstract

The need of reconstructing discrete-valued sparse signals from few measurements, that is solving an undetermined system of linear equations, appears frequently in science and engineering. Whereas classical compressed sensing algorithms do not incorporate the additional knowledge of the discrete nature of the signal, classical lattice decoding approaches such as the sphere decoder do not utilize sparsity constraints. In this work, we present an approach that incorporates a discrete values prior into basis pursuit. In particular, we address unipolar binary and bipolar ternary sparse signals, i.e., sparse signals with entries in $\{0,1\}$, respectively in $\{-1,0,1\}$. We will show that phase transition takes place earlier than when using the classical basis pursuit approach and that, independently of the sparsity of the signal, at most $N/2$, respectively $3N/4$, measurements are necessary to recover a unipolar binary, and a bipolar ternary signal uniquely, where $N$ is the dimension of the ambient space. We will further discuss robustness of the algorithm and generalizations to signals with entries in larger alphabets.