Data for: Spectrum Sensing with a Parallel Algorithm for Cyclostationary Feature Extraction
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:
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
The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.