Compressive energy detection for blind coarse wideband sensing: Comparative performance study

Lavrenko, Anastasia; Thomä, Reiner S.; Bollig, Andreas

doi:10.1109/iswcs.2015.7454392

Cited by 2 publications

(3 citation statements)

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“…With high probability, this produces the same result since for most large underdetermined systems of linear equations the minimal 1 -norm solution is also the sparsest solution [24]. Another way of solving (21) efficiently is applying one of the many greedy sparse recovery algorithms that have been developed in the field of CS, such as, e. g., orthogonal matching pursuit (OMP).…”

Section: Simultaneous Omp Ca Estimationmentioning

confidence: 95%

“…In [17], the modulated wideband converter (MWC) [18] is used to obtain the SC from sub-Nyquist samples to then apply cyclostationarity detection. Apart from cyclostationarity, CS has seen utilization in different branches of spectrum sensing as for example in energy detection [19,20,21].…”

Section: Arxiv:161004027v1 [Csit] 13 Oct 2016mentioning

confidence: 99%

“…OMP is a greedy algorithm that iteratively determines a vector's support from an underdetermined system of linear equations and subsequently recovers the vector by solving a least-squares problem. Using it, we could solve (21) for each column of Rx individually (as in [13]), i. e., we could solve min rν…”

Section: Simultaneous Omp Ca Estimationmentioning

confidence: 99%

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Compressive cyclostationary spectrum sensing with a constant false alarm rate

Bollig

Lavrenko

Arts

et al. 2017

J Wireless Com Network

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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.

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Section: Simultaneous Omp Ca Estimationmentioning

confidence: 95%