Stationary Graph Processes

Parametric Power Spectral Estimation

Conference paper (2017)

Authors

Santiago Segarra Massachusetts Institute of Technology

Antonio G. Marques King Juan Carlos University

G.J.T. Leus Signal Processing Systems -

Alejandro Ribeiro University of Pennsylvania

Research Group

Signal Processing Systems () (TU Delft)

Power spectral density Graph signal processing Parametric estimation ARMA graph processes Stationarity

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Published Date

2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Advancing a holistic theory of networks and network processes requires the extension of existing results in the processing of time-varying signals to signals supported on graphs. This paper focuses on the definition of stationarity and power spectral density for random graph signals, generalizes the concepts of autoregressive and moving average random processes to the graph domain, and investigates their parametric spectral estimation. Theoretical and algorithmic results are complemented with numerical tests on synthetic and real-world graphs.

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