Exact topology learning in a network of cyclostationary processes

Harish Doddi, Saurav Talukdar, Deepjyoti Deka, Murti Salapaka

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

1 Scopus citations


Learning the structure of a network from time-series data, in particular cyclostationary data, is of significant interest in many disciplines such as power grids, biology, finance. In this article, an algorithm is presented for reconstruction of the topology of a network of cyclostationary processes. To the best of our knowledge, this is the first work to guarantee exact recovery without any assumptions on the underlying structure. The method is based on a lifting technique by which cyclostationary processes are mapped to vector wide sense stationary processes and further on semi-definite properties of matrix Wiener filters for the said processes. We demonstrate the performance of the proposed algorithm on a Resistor-Capacitor network and present the accuracy of reconstruction for varying sample sizes.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538679265
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2019 American Automatic Control Council.


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