Distributed Recursive Least Squares Strategies for Adaptive Reconstruction of Graph Signals

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

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

13 Citations (Scopus)


This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target steady-state performance. Then, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. The performed numerical tests show the performance of the proposed adaptive method for distributed learning of graph signals.
Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-0-9928626-7-1
Publication statusPublished - 2017
EventEUSIPCO 2017: 25th European Signal Processing Conference - Kos Island, Greece
Duration: 28 Aug 20172 Sept 2017
Conference number: 25


ConferenceEUSIPCO 2017
Abbreviated titleEUSIPCO
CityKos Island
Internet address


  • Recursive least squares estimation
  • graph signal processing
  • sampling
  • adaptive networks


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