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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.
Cited literature [16 references]
https://hal-imt-atlantique.archives-ouvertes.fr/hal-02280735
Contributor : Nicolas Grollier <>
Submitted on : Friday, September 6, 2019 - 3:14:22 PM
Last modification on : Monday, October 5, 2020 - 9:50:27 AM
Long-term archiving on: : Thursday, February 6, 2020 - 6:41:21 PM
Contributor : Nicolas Grollier <>
Submitted on : Friday, September 6, 2019 - 3:14:22 PM
Last modification on : Monday, October 5, 2020 - 9:50:27 AM
Long-term archiving on: : Thursday, February 6, 2020 - 6:41:21 PM
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ICT_2018_VG_AN_NG_JP_SD_v2.pdf
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