Blind Deconvolution of Nonstationary Graph Signals over Shift-Invariant Channels

TL;DR

This paper introduces a method for blind deconvolution of nonstationary graph signals transmitted over unknown shift-invariant channels, utilizing covariance structures, and demonstrates its effectiveness with temperature data experiments.

Contribution

It presents a novel blind deconvolution approach for nonstationary graph signals that leverages covariance information and handles unknown channels.

Findings

Effective channel estimation demonstrated

Successful deconvolution of nonstationary signals

Validated with real temperature data

Abstract

In this paper, we investigate blind deconvolution of nonstationary graph signals from noisy observations, transmitted through an unknown shift-invariant channel. The deconvolution process assumes that the observer has access to the covariance structure of the original graph signals. To evaluate the effectiveness of our channel estimation and blind deconvolution method, we conduct numerical experiments using a temperature dataset in the Brest region of France.