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New Blind Free-Band Detectors Exploiting Cyclic Autocorrelation Function Sparsity

Abstract : In this chapter, we will firstly show that the Cyclic Autocorrelation Function (CAF) of a lineary modulated signal is a sparse function in the cyclic frequency domain. Then using this property we propose a new CAF estimator, using compressed sensing technique with the Orthogonal Matching Pursuit (OMP) algorithm. This new proposed estimator outperforms the classic estimator used in [1] under the same conditions, using the same number of samples. Furthermore, since our estimator does not need any information, we claim that it is a blind estimator whereas the estimator of [1] is clearly not blind because it needs the knowledge of the cyclic frequency. Many cases will be analysed: with and without the impact of a propagation channel at the reception. Using this new CAF estimator we propose two blind free bands detectors in the second part of this chapter. The first one is a soft version of the algorithm proposed in [2], that assumes that two estimated CAF of two successive packets of samples, should have close cyclic frequencies. The second one [3] uses Symmetry Property of the Second Order Cyclic Autocorrelation. Both methods outperform the cyclostationnarity detector of Dantawate-Giannakis of [1]. The second method outperforms the first one. Finally we study the complexity of the new proposed detectors and compare it to the complexity of [1].
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Submitted on : Tuesday, June 2, 2015 - 4:16:48 PM
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Ziad Khalaf, Jacques Palicot. New Blind Free-Band Detectors Exploiting Cyclic Autocorrelation Function Sparsity. Maria-Gabriella Di Benedetto, Faouzi Bader. Cognitive Communication and Cooperative HetNet Coexistence, Springer, pp.91-117, 2014, 978-3-319-01401-2. ⟨10.1007/978-3-319-01402-9_5⟩. ⟨hal-01159134⟩



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