Autoregressive Moving Average Graph Filters a Stable Distributed Implementation

Elvin Isufi*, Andreas Loukas, Geert Leus

*Corresponding author for this work

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

10 Citations (Scopus)


We present a novel implementation strategy for distributed autoregressive moving average (ARMA) graph filters. Differently from the state of the art implementation, the proposed approach has the following benefits: (i) the designed filter coefficients come with stability guarantees, (ii) the linear convergence time can now be controlled by the filter coefficients, and (iii) the stable filter coefficients that approximate a desired frequency response are optimal in a least squares sense. Numerical results show that the proposed implementation outperforms the state of the art distributed infinite impulse response (IIR) graph filters. Further, even at fixed distributed costs, compared with the popular finite impulse response (FIR) filters, at high orders our method achieves tighter low-pass responses, suggesting that it should be preferable in accuracy-demanding applications.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-1-5090-4117-6
Publication statusPublished - 2017
EventICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States
Duration: 5 Mar 20179 Mar 2017
Conference number: 42


ConferenceICASSP 2017
Abbreviated titleICASSP
Country/TerritoryUnited States
CityNew Orleans, LA
Internet address


  • autoregressive moving average graph filters
  • graph filters
  • graph signal processing


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