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Cyclic Autocorrelation based Spectrum Sensing: Theoretical Derivation Framework

Abstract : In this article, we propose a theoretical framework to derive the stochastic behavior of the cyclic autocorrelation power (CAP). This function is especially used in cyclostationarity-based spectrum sensing for its robustness to noise uncertainty and its low computational cost. We first express the theoretical probability density function (PDF) of the cyclic autocorrelation power-which proves to follow a central scaled (respectively non-central) chi-square distribution if the received samples consist of additive Gaussian noise (respectively noise plus a cyclostationary signal). In order to verify the accuracy of the proposed theoretical derivation, simulation results are then provided in terms of detection and false alarm probabilities.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02280735
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Vincent Gouldieff, Amor Nafkha, Nicolas Grollier, Jacques Palicot, Steredenn Daumont. Cyclic Autocorrelation based Spectrum Sensing: Theoretical Derivation Framework. 2018 25th International Conference on Telecommunications (ICT), Jun 2018, Saint-Malo, France. ⟨10.1109/ICT.2018.8464887⟩. ⟨hal-02280735⟩

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