An Information Theoretic Study for Noisy Compressed Sensing With Joint Sparsity Model-2

TL;DR

This paper provides an information theoretic analysis of support set reconstruction in noisy joint sparsity models, deriving bounds and conditions that highlight the advantages of joint sparsity in compressed sensing.

Contribution

It introduces bounds and necessary conditions for support recovery in noisy JSM-2, revealing its potential to require fewer measurements than traditional models.

Findings

Derived upper and lower bounds on failure probability.

Established necessary and sufficient conditions for support recovery.

Showed that noisy JSM-2 can outperform noisy MMV in measurement efficiency.

Abstract

In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction.