Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction

TL;DR

This paper introduces a hierarchical sparse recovery approach for blind deconvolution and demixing, providing the first rigorous guarantees for efficient algorithms in bi-sparse settings with practical applications.

Contribution

It develops a novel hierarchical sparse recovery framework for blind deconvolution and demixing, with theoretical guarantees and numerical validation, addressing a gap in existing literature.

Findings

Recovery guarantees for bi-sparse blind deconvolution and demixing

Hierarchical sparse recovery framework enables efficient algorithms

Numerical results suggest better practical performance than theoretical bounds

Abstract

The blind deconvolution problem amounts to reconstructing both a signal and a filter from the convolution of these two. It constitutes a prominent topic in mathematical and engineering literature. In this work, we analyze a sparse version of the problem: The filter $h\in \mathbb{R}^\mu$ is assumed to be $s$-sparse, and the signal $b \in \mathbb{R}^n$ is taken to be $\sigma$-sparse, both supports being unknown. We observe a convolution between the filter and a linear transformation of the signal. Motivated by practically important multi-user communication applications, we derive a recovery guarantee for the simultaneous demixing and deconvolution setting. We achieve efficient recovery by relaxing the problem to a hierarchical sparse recovery for which we can build on a flexible framework. At the same time, for this we pay the price of some sub-optimal guarantees compared to the number of free parameters of the problem. The signal model we consider is sufficiently general to capture many applications in a number of engineering fields. Despite their practical importance, we provide first rigorous performance guarantees for efficient and simple algorithms for the bi-sparse and generalized demixing setting. We complement our analytical results by presenting results of numerical simulations. We find evidence that the sub-optimal scaling $s^2\sigma \log(\mu)\log(n)$ of our derived sufficient condition is likely overly pessimistic and that the observed performance is better described by a scaling proportional to $ s\sigma$ up to log-factors.