Dictionary-based reconstruction of the cyclic autocorrelation via &amp;#x2113;&lt;inf&gt;1&lt;/inf&gt;-minimization for cyclostationary spectrum sensing

Bollig, Andreas; Mathar, Rudolf

doi:10.1109/icassp.2013.6638594

Cited by 7 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [31], a method for estimating the cyclic autocorrelation function using a short observation interval is presented. Cyclic spectrum estimation for wide-sense cyclostationary signals sampled at sub-Nyquist rate is made in [57] by uniform sampling and in [18,382] by non-uniform (random) sampling.…”

Section: Compressive Sensingmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

View full text Add to dashboard Cite

Section: Compressive Sensingmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

View full text Add to dashboard Cite

“…Another one is the symmetry of the CA around the DC component. First steps in this direction showing promising results have been taken in [26]. The drawback of the solution proposed in [26] is that the convex optimization problem used to recover the CA becomes huge for practical parameter choices, which results in a prohibitively large computational complexity.…”

Section: Dictionary Assisted Ca Estimationmentioning

confidence: 99%

“…First steps in this direction showing promising results have been taken in [26]. The drawback of the solution proposed in [26] is that the convex optimization problem used to recover the CA becomes huge for practical parameter choices, which results in a prohibitively large computational complexity. To circumvent this we propose an OMP-based greedy algorithm that takes advantage of the additional prior knowledge while featuring a much smaller complexity than the optimization problem.…”

Section: Dictionary Assisted Ca Estimationmentioning

confidence: 99%

Compressive cyclostationary spectrum sensing with a constant false alarm rate

Bollig

Lavrenko

Arts

et al. 2017

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

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.

show abstract