Distributed Wiener-Based Reconstruction of Graph Signals

Conference paper (2018)

Authors

Elvin Isufi Signal Processing Systems, University of Perugia
Paolo Di Lorenzo University of Perugia
Paolo Banelli University of Perugia
Geert Leus Signal Processing Systems
Research Group
Signal Processing Systems
Copyright: © 2018 E. Isufi, Paolo Di Lorenzo, Paolo Banelli, G.J.T. Leus
DOI: https://doi.org/10.1109/SSP.2018.8450828
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Published Date

2018

Language

English

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Research Group

Signal Processing Systems

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.

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