Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing

Shusuke NARIEDA; Daiki CHO; Hiromichi OGASAWARA; Kenta UMEBAYASHI; Takeo FUJII; Hiroshi NARUSE

doi:10.1587/transcom.2019EBP3175

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Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing

Shusuke NARIEDA, Daiki CHO, Hiromichi OGASAWARA, Kenta UMEBAYASHI, Takeo FUJII, Hiroshi NARUSE

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Keywords: cognitive radio, maximum cyclic autocorrelation selection based spectrum sensing, signal cyclostationary

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2020 Volume E103.B Issue 12 Pages 1462-1469

DOI https://doi.org/10.1587/transcom.2019EBP3175

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Abstract

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.

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