Distributed Wiener-Based Reconstruction of Graph Signals

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-1-5386-1570-3
ISBN (Print) 978-1-5386-1572-0
Publication statusPublished - 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018


Conference20th IEEE Statistical Signal Processing Workshop, SSP 2018
CityFreiburg im Breisgau


  • ARMA graph filters
  • Graph signal processing
  • stationary graph signals
  • Wiener regularization


Dive into the research topics of 'Distributed Wiener-Based Reconstruction of Graph Signals'. Together they form a unique fingerprint.

Cite this