Stationary Graph Processes

Nonparametric Spectral Estimation

Conference paper (2016)

Authors

Santiago Segarra University of Pennsylvania
Antonio G. Marques King Juan Carlos University
G Leus Signal Processing Systems - EEMCS
Alejandro Ribeiro University of Pennsylvania
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
More Info
expand_more

Published Date

19-09-2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides in more irregular graph domains. The contribution in this paper is twofold. First, we propose several equivalent notions of weak stationarity for random graph signals, all taking into account the structure of the graph where the random process takes place. Second, we analyze the properties of the induced power spectral density along with nonparametric approaches to estimate it, including average and window-based periodograms.