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Proceedings Paper

CHOCS: a framework for estimating compressive higher order cyclostationary statistics
Author(s): Chia Wei Lim; Michael B. Wakin
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Paper Abstract

The framework of computing Higher Order Cyclostationary Statistics (HOCS) from an incoming signal has proven useful in a variety of applications over the past half century, from Automatic Modulation Recognition (AMR) to Time Dierence of Arrival (TDOA) estimation. Much more recently, a theory known as Compressive Sensing (CS) has emerged that enables the ecient acquisition of high-bandwidth (but sparse) signals via nonuni- form low-rate sampling protocols. While most work in CS has focused on reconstructing the high-bandwidth signals from nonuniform low-rate samples, in this work, we consider the task of inferring the modulation of a communications signal directly in the compressed domain, without requiring signal reconstruction. We show that the HOCS features used for AMR are compressible in the Fourier domain, and hence, that AMR of various linearly modulated signals is possible by estimating the same HOCS features from nonuniform compressive sam- ples. We provide analytical support for the accurate approximation of HOCS features from nonuniform samples and derive practical rules for classication of modulation type using these samples based on simulated data.

Paper Details

Date Published: 8 June 2012
PDF: 13 pages
Proc. SPIE 8365, Compressive Sensing, 83650M (8 June 2012); doi: 10.1117/12.918262
Show Author Affiliations
Chia Wei Lim, Colorado School of Mines (United States)
Michael B. Wakin, Colorado School of Mines (United States)


Published in SPIE Proceedings Vol. 8365:
Compressive Sensing
Fauzia Ahmad, Editor(s)

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