Sparse volterra systems: Theory and practice

Andrew Bolstad, Benjamin A. Miller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nonlinear effects limit analog circuit performance, causing both in-band and out-of-band distortion. The classical Volterra series provides an accurate model of many nonlinear systems, but the number of parameters grows extremely quickly as the memory depth and polynomial order are increased. Recently, concepts from compressed sensing have been applied to nonlinear system modeling in order to address this issue. This work investigates the theory and practice of applying compressed sensing techniques to nonlinear system identification under the constraints of typical radio frequency (RF) laboratories. The main theoretical result shows that these techniques are capable of identifying sparse Memory Polynomials using only single-tone training signals rather than pseudorandom noise. Empirical results using laboratory measurements of an RF receiver show that sparse Generalized Memory Polynomials can also be recovered from two-tone signals.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages5740-5744
Number of pages5
DOIs
StatePublished - 18 Oct 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • compressed sensing
  • compressive sensing
  • nonlinear equalization
  • Nonlinear system identification
  • sparse modeling

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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