Graph filter design for distributed network processing: a comparison between adaptive algorithms

Research output: Contribution to conferencePaperpeer-review

Abstract

Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate using GFs to implement signal processing operations in a distributed manner. However, in most GF models, the input signals are assumed to be time-invariant, static, or change at a very low rate. In addition to that, the GF coefficients are usually set to be node-invariant, i.e. the same for all the nodes. Yet, in general, the input signals may evolve with time and the underlying GF may have parameters dependent on the nodes. Therefore, in this paper, we consider dynamic input signals with two sets of GF coefficients, node-variant, i.e. vary on different nodes, and node-invariant. Then, we apply LMS and RLS algorithms for GF design, along with two others called adapt-then-combine (ATC) and combined RLS (CRLS) to estimate the GF coefficients. We study and compare the performance of the algorithms and show that in the case of node-invariant GF coefficients, CRLS gives the best performance with lowest mean-square-displacement (MSD), whereas, for node-variant case, RLS represents the best results. The effect of bias in the input signal has also been examined.
Original languageEnglish
Number of pages5
Publication statusPublished - 15 Sep 2021
EventInternational Conference in Sensor Signal Processing for Defence: from Sensor to Decision - Edinburgh, United Kingdom
Duration: 14 Sep 202115 Sep 2021
Conference number: 10
https://sspd.eng.ed.ac.uk

Conference

ConferenceInternational Conference in Sensor Signal Processing for Defence
Abbreviated titleSSPD
Country/TerritoryUnited Kingdom
CityEdinburgh
Period14/09/2115/09/21
Internet address

Keywords

  • graph signal processing (GSP)
  • graph filtering
  • distributed processing
  • adaptive algorithms

Fingerprint

Dive into the research topics of 'Graph filter design for distributed network processing: a comparison between adaptive algorithms'. Together they form a unique fingerprint.

Cite this