Dictionary-based reconstruction of the cyclic autocorrelation via l1-minimization

Authors

A. Bollig, R. Mathar,

Abstract

        One of the main enablers of dynamic spectrum access is fast and reliable spectrum sensing. Acquiring the occupation status of a spectral band can be accomplished in different ways, one of which is called cyclostationary spectrum sensing. The aforementioned method exploits the prior knowledge of periodicities inherent in most man-made signals for the purpose of detecting their presence in a set of sample data. One pre-requisite for the detection is the knowledge of the signal's cyclic autocorrelation (CA), which can be estimated from a finite amount of time-domain samples. This work introduces a new method for estimating the CA using a very small amount of time-domain samples, i.e. a short observation time. This is accomplished by modeling the desired CA vector using a custom dictionary describing its known properties and recovering it by solving a convex optimization problem.

BibTEX Reference Entry 

@inproceedings{BoMa13,
	author = {Andreas Bollig and Rudolf Mathar},
	title = "Dictionary-based reconstruction of the cyclic autocorrelation via l1-minimization",
	pages = "4908-4912",
	booktitle = "The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)",
	address = {Vancouver, Canada},
	month = May,
	year = 2013,
	hsb = hsb999910305961,
	}

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