Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing

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

IEICE TRANSACTIONS on Communications   Vol.E103-B   No.12   pp.1462-1469
Publication Date: 2020/12/01
Publicized: 2020/06/22
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2019EBP3175
Type of Manuscript: PAPER
Category: Terrestrial Wireless Communication/Broadcasting Technologies
cognitive radio,  maximum cyclic autocorrelation selection based spectrum sensing,  signal cyclostationary,  

Full Text: PDF>>
Buy this Article

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.