Stationary Graph Processes: Parametric Power Spectral Estimation

Santiago Segarra; Antonio G. Marques; Geert Leus; Alejandro Ribeiro

doi:10.1109/ICASSP.2017.7952927

Stationary Graph Processes: Parametric Power Spectral Estimation

Santiago Segarra, Antonio G. Marques, Geert Leus, Alejandro Ribeiro

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	4099-4103
Number of pages	5
ISBN (Electronic)	978-1-5090-4117-6
DOIs	https://doi.org/10.1109/ICASSP.2017.7952927
Publication status	Published - 2017
Event	ICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States Duration: 5 Mar 2017 → 9 Mar 2017 Conference number: 42 http://www.ieee-icassp2017.org/

Conference

Conference	ICASSP 2017
Abbreviated title	ICASSP
Country/Territory	United States
City	New Orleans, LA
Period	5/03/17 → 9/03/17
Internet address	http://www.ieee-icassp2017.org/

Keywords

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

Access to Document

10.1109/ICASSP.2017.7952927

http://ens.ewi.tudelft.nl/pubs/leus17icassp2.pdf

Cite this

@inproceedings{3ede306801414150b7d79b9fc2bb7495,

title = "Stationary Graph Processes: Parametric Power Spectral Estimation",

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.",

keywords = "ARMA graph processes, Graph signal processing, Parametric estimation, Power spectral density, Stationarity",

author = "Santiago Segarra and Marques, {Antonio G.} and Geert Leus and Alejandro Ribeiro",

year = "2017",

doi = "10.1109/ICASSP.2017.7952927",

language = "English",

pages = "4099--4103",

booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "ICASSP 2017 : 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals, ICASSP ; Conference date: 05-03-2017 Through 09-03-2017",

url = "http://www.ieee-icassp2017.org/",

}

Segarra, S, Marques, AG, Leus, G & Ribeiro, A 2017, Stationary Graph Processes: Parametric Power Spectral Estimation. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7952927, IEEE, Piscataway, NJ, pp. 4099-4103, ICASSP 2017, New Orleans, LA, United States, 5/03/17. https://doi.org/10.1109/ICASSP.2017.7952927

Stationary Graph Processes: Parametric Power Spectral Estimation. / Segarra, Santiago; Marques, Antonio G.; Leus, Geert et al.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Piscataway, NJ: IEEE, 2017. p. 4099-4103 7952927.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Stationary Graph Processes

T2 - ICASSP 2017

AU - Segarra, Santiago

AU - Marques, Antonio G.

AU - Leus, Geert

AU - Ribeiro, Alejandro

N1 - Conference code: 42

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - ARMA graph processes

KW - Graph signal processing

KW - Parametric estimation

KW - Power spectral density

KW - Stationarity

UR - http://www.scopus.com/inward/record.url?scp=85023768174&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2017.7952927

DO - 10.1109/ICASSP.2017.7952927

M3 - Conference contribution

AN - SCOPUS:85023768174

SP - 4099

EP - 4103

BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings

PB - IEEE

CY - Piscataway, NJ

Y2 - 5 March 2017 through 9 March 2017

ER -

Stationary Graph Processes: Parametric Power Spectral Estimation

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this