Time-scale canonical model for wideband system characterization

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

7 Scopus citations

Abstract

In this paper, we propose a time-scale canonical model as a discrete characterization of wideband linear time-varying systems. This representation decomposes a system output into discrete time shifts and Doppler scalings on the input, weighted by a smoothed discrete version of the wideband spreading function. We base this formulation on the Mellin transform that is matched to scalings. We also demonstrate that our proposed model inherently affords a joint multipath-scale diversity in wideband communication channels. By properly designing the signaling and reception schemes using wavelet techniques, we can achieve this diversity over a dyadic time-scale framework.

Original languageEnglish (US)
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesIV281-IV284
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

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

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Jiang, Y., & Papandreou-Suppappola, A. (2005). Time-scale canonical model for wideband system characterization. In 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions (pp. IV281-IV284). [1416000] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. IV). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2005.1416000