Distributed Wiener-Based Reconstruction of Graph Signals

Elvin Isufi; Paolo Di Lorenzo; Paolo Banelli; Geert Leus

doi:10.1109/SSP.2018.8450828

Distributed Wiener-Based Reconstruction of Graph Signals

Elvin Isufi, Paolo Di Lorenzo, Paolo Banelli, Geert Leus

Signal Processing Systems

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

14 Citations (Scopus)

18 Downloads (Pure)

Abstract

This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.

Original language	English
Title of host publication	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	21-25
Number of pages	5
ISBN (Electronic)	978-1-5386-1570-3
ISBN (Print)	978-1-5386-1572-0
DOIs	https://doi.org/10.1109/SSP.2018.8450828
Publication status	Published - 2018
Event	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 10 Jun 2018 → 13 Jun 2018

Conference

Conference	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/Territory	Germany
City	Freiburg im Breisgau
Period	10/06/18 → 13/06/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

ARMA graph filters
Graph signal processing
stationary graph signals
Wiener regularization

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10.1109/SSP.2018.8450828

Distributed_Wiener-Based_Reconstruction_of_Graph_SignalsFinal published version, 693 KB

Cite this

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title = "Distributed Wiener-Based Reconstruction of Graph Signals",

abstract = "This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.",

keywords = "ARMA graph filters, Graph signal processing, stationary graph signals, Wiener regularization",

author = "Elvin Isufi and {Di Lorenzo}, Paolo and Paolo Banelli and Geert Leus",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 20th IEEE Statistical Signal Processing Workshop, SSP 2018 ; Conference date: 10-06-2018 Through 13-06-2018",

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doi = "10.1109/SSP.2018.8450828",

language = "English",

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AU - Isufi, Elvin

AU - Di Lorenzo, Paolo

AU - Banelli, Paolo

AU - Leus, Geert

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2018

Y1 - 2018

N2 - This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.

AB - This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.

KW - ARMA graph filters

KW - Graph signal processing

KW - stationary graph signals

KW - Wiener regularization

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U2 - 10.1109/SSP.2018.8450828

DO - 10.1109/SSP.2018.8450828

M3 - Conference contribution

AN - SCOPUS:85053858401

SN - 978-1-5386-1572-0

SP - 21

EP - 25

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - IEEE

CY - Piscataway, NJ

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -

Distributed Wiener-Based Reconstruction of Graph Signals

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Bibliographical note

Keywords

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