Distributed Wiener-Based Reconstruction of Graph Signals

Distributed Wiener-Based Reconstruction of Graph Signals

Isufi, E. (TU Delft Signal Processing Systems; University of Perugia)
Di Lorenzo, Paolo (University of Perugia)
Banelli, Paolo (University of Perugia)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2018

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.

ARMA graph filters
Graph signal processing
stationary graph signals
Wiener regularization

http://resolver.tudelft.nl/uuid:682521a8-561e-4529-ab22-77b8f88fbc4f

https://doi.org/10.1109/SSP.2018.8450828

IEEE, Piscataway, NJ

2019-03-01

978-1-5386-1572-0

2018 IEEE Statistical Signal Processing Workshop, SSP 2018

20th IEEE Statistical Signal Processing Workshop, SSP 2018, 2018-06-10 → 2018-06-13, Freiburg im Breisgau, Germany

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.

Institutional Repository

conference paper

© 2018 E. Isufi, Paolo Di Lorenzo, Paolo Banelli, G.J.T. Leus