Compressive cyclostationary spectrum sensing with a constant false alarm rate

Authors

A. Bollig, A. Lavrenko, M. Arts, R. Mathar,

Abstract

        Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.

Open Access Attribution 4.0 International (CC BY 4.0)

BibTEX Reference Entry 

@article{BoLaArMa17,
	author = {Andreas Bollig and Anastasia Lavrenko and Martijn Arts and Rudolf Mathar},
	title = "Compressive cyclostationary spectrum sensing with a constant false alarm rate",
	pages = "135-148",
	journal = "EURASIP Journal on Wireless Communications and Networking",
	volume = "2017",
	number = "1",
	doi = 10.1186/s13638-017-0920-5,
	month = Aug,
	year = 2017,
	}

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© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.