Volterra kernels extraction from frequency-domain data for weakly non-linear circuit time-domain simulation

Thong Nguyen, Jose E. Schutt-Aine, Ying Chen

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

Abstract

Volterra series have been popular in non-linear system analysis and modeling for a long time. Volterra representation adapts the convolution concept from linear time-invariant (LTI) systems, generalizes it to 'super convolutions' and, hence, are able to characterize the dynamical behavior of non-linear circuits using familiar LTI techniques. It is extensively used in white-box verification problems where small order distortion terms occur such as transistor circuits. This paper presents an experiment with finite order non-linear circuits and uses frequency-domain data obtained from Harmonic Balance (HB) simulation to extract Volterra kernels.

Original languageEnglish (US)
Title of host publication2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-2
Number of pages2
ISBN (Electronic)9789994904013
DOIs
StatePublished - Dec 29 2017
Event2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017 - Cape Town, South Africa
Duration: Sep 25 2017Sep 28 2017

Publication series

Name2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017
Volume2017-January

Other

Other2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017
CountrySouth Africa
CityCape Town
Period9/25/179/28/17

    Fingerprint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation

Cite this

Nguyen, T., Schutt-Aine, J. E., & Chen, Y. (2017). Volterra kernels extraction from frequency-domain data for weakly non-linear circuit time-domain simulation. In 2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017 (pp. 1-2). (2017 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/RADIO.2017.8242244