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 |
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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 | |
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 |
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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-careOtherwise 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