Polynomial graph filter of multiple shifts and distributed implementation of inverse filtering

TL;DR

This paper proposes distributed iterative algorithms for inverse polynomial graph filters with multiple shifts, enabling efficient denoising of time-varying graph signals in networked systems.

Contribution

It introduces two novel distributed iterative algorithms for inverse polynomial graph filtering with multiple shifts, addressing computational challenges.

Findings

Algorithms effectively denoise time-varying graph signals.

Distributed implementation reduces computational burden.

Successful application to US temperature data set.

Abstract

Polynomial graph filters and their inverses play important roles in graph signal processing. An advantage of polynomial graph filters is that they can be implemented in a distributed manner, which involves data transmission between adjacent vertices only. The challenge arisen in the inverse filtering is that a direct implementation may suffer from high computational burden, as the inverse graph filter usually has full bandwidth even if the original filter has small bandwidth. In this paper, we consider distributed implementation of the inverse filtering procedure for a polynomial graph filter of multiple shifts, and we propose two iterative approximation algorithms that can be implemented in a distributed network, where each vertex is equipped with systems for limited data storage, computation power and data exchanging facility to its adjacent vertices. We also demonstrate the effectiveness of the proposed iterative approximation algorithms to implement the inverse filtering procedure and their satisfactory performance to denoise time-varying graph signals and a data set of US hourly temperature at 218 locations.