Data for: Spectrum Sensing with a Parallel Algorithm for Cyclostationary Feature Extraction

Published: 2 Aug 2018 | Version 1 | DOI: 10.17632/n92996yz3m.1
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Description of this data

Cyclostationary feature detection is one of the most common methods used in spectrum sensing. The parallel algorithm presented here can be used in multi-core processors to speedup the execution time. The implementation uses OpenMP and the Fastest Fourier Transform in the West (a efficient Fourier transform method).

Experiment data files

This data is associated with the following publication:

Spectrum sensing with a parallel algorithm for cyclostationary feature extraction

Published in: Computers and Electrical Engineering

Latest version

  • Version 1

    2018-08-02

    Published: 2018-08-02

    DOI: 10.17632/n92996yz3m.1

    Cite this dataset

    Lima, Arthur; Silveira, Luiz; Xavier-de-Souza, Samuel (2018), “Data for: Spectrum Sensing with a Parallel Algorithm for Cyclostationary Feature Extraction”, Mendeley Data, v1 http://dx.doi.org/10.17632/n92996yz3m.1

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Categories

Parallel Processing, Sensing Mechanisms

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