Decentralized regression with asynchronous sub-Nyquist sampling

Hoi To Wai, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution


When capturing data on a sensor field to uncover its latent structure, there are often nuisance parameters in the observation model that turn even linear regression problems into non-convex optimizations. One common case is the lack of common timing source in ADCs, therefore samplings are done with time offsets. Motivated by the desire of estimating jointly the sensor field and nuisance parameters in a wide area deployment, this paper derives a new decentralized algorithm that combines alternating optimization and gossip-based learning. The proposed algorithm is shown to converge to the neighborhood of a local minimum, both analytically and empirically.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479982974
StatePublished - Apr 24 2015
Externally publishedYes
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove


  • asynchronous sampling
  • decentralized regression
  • gossip algorithm
  • sub-Nyquist sampling

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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