Compressive cyclostationary spectrum sensing with a constant false alarm rate
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...
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doaj-103806c445d74ac4845456e78a3795032020-11-24T21:55:34ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992017-08-012017111310.1186/s13638-017-0920-5Compressive cyclostationary spectrum sensing with a constant false alarm rateAndreas Bollig0Anastasia Lavrenko1Martijn Arts2Rudolf Mathar3Institute for Theoretical Information Technology, RWTH Aachen UniversityInstitute for Information Technology, TU IlmenauInstitute for Theoretical Information Technology, RWTH Aachen UniversityInstitute for Theoretical Information Technology, RWTH Aachen UniversityAbstract 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.http://link.springer.com/article/10.1186/s13638-017-0920-5CyclostationaritySpectrum sensingSparse recovery |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andreas Bollig Anastasia Lavrenko Martijn Arts Rudolf Mathar |
spellingShingle |
Andreas Bollig Anastasia Lavrenko Martijn Arts Rudolf Mathar Compressive cyclostationary spectrum sensing with a constant false alarm rate EURASIP Journal on Wireless Communications and Networking Cyclostationarity Spectrum sensing Sparse recovery |
author_facet |
Andreas Bollig Anastasia Lavrenko Martijn Arts Rudolf Mathar |
author_sort |
Andreas Bollig |
title |
Compressive cyclostationary spectrum sensing with a constant false alarm rate |
title_short |
Compressive cyclostationary spectrum sensing with a constant false alarm rate |
title_full |
Compressive cyclostationary spectrum sensing with a constant false alarm rate |
title_fullStr |
Compressive cyclostationary spectrum sensing with a constant false alarm rate |
title_full_unstemmed |
Compressive cyclostationary spectrum sensing with a constant false alarm rate |
title_sort |
compressive cyclostationary spectrum sensing with a constant false alarm rate |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2017-08-01 |
description |
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. |
topic |
Cyclostationarity Spectrum sensing Sparse recovery |
url |
http://link.springer.com/article/10.1186/s13638-017-0920-5 |
work_keys_str_mv |
AT andreasbollig compressivecyclostationaryspectrumsensingwithaconstantfalsealarmrate AT anastasialavrenko compressivecyclostationaryspectrumsensingwithaconstantfalsealarmrate AT martijnarts compressivecyclostationaryspectrumsensingwithaconstantfalsealarmrate AT rudolfmathar compressivecyclostationaryspectrumsensingwithaconstantfalsealarmrate |
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