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.

Distributed wiener-based reconstruction of graph signals / Isufi, Elvin; DI LORENZO, Paolo; Banelli, Paolo; Leus, Geert. - (2018), pp. 673-677. (Intervento presentato al convegno 20th IEEE Statistical Signal Processing Workshop, SSP 2018 tenutosi a Freiburg; Germany) [10.1109/SSP.2018.8450828].

Distributed wiener-based reconstruction of graph signals

Paolo Di Lorenzo;
2018

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.
2018
20th IEEE Statistical Signal Processing Workshop, SSP 2018
ARMA graph filters; graph signal processing; stationary graph signals; wiener regularization; signal processing; instrumentation; computer networks and communications
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Distributed wiener-based reconstruction of graph signals / Isufi, Elvin; DI LORENZO, Paolo; Banelli, Paolo; Leus, Geert. - (2018), pp. 673-677. (Intervento presentato al convegno 20th IEEE Statistical Signal Processing Workshop, SSP 2018 tenutosi a Freiburg; Germany) [10.1109/SSP.2018.8450828].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1163701
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