Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing

被引：0

作者：

Narieda, Shusuke ^{[1
]}

Cho, Daiki ^{[2
,5
]}

Ogasawara, Hiromichi ^{[3
]}

Umebayashi, Kenta ^{[2
]}

Fujii, Takeo ^{[4
]}

Naruse, Hiroshi ^{[1
]}

机构：

[1] Mie Univ, Fac Engn, Dept Inform Eng, Tsu, Mie 5148507, Japan

[2] Tokyo Univ Agr & Technol, Dept Elect & Electron Engn, Koganei, Tokyo 1848588, Japan

[3] Akashi Coll, Nat Inst Technol, Dept Gen Studies, Akashi, Hyogo 6748501, Japan

[4] Univ Electrocommun, Adv Wireless & Commun Res Ctr AWCC, Chofu, Tokyo 1828585, Japan

[5] Panasonic Corp, Kadoma, Osaka, Japan

来源：

IEICE TRANSACTIONS ON COMMUNICATIONS | 2020年 / E103B卷 / 12期

关键词：

cognitive radio; maximum cyclic autocorrelation selection based spectrum sensing; signal cyclostationary; COGNITIVE RADIO;

D O I：

10.1587/transcom.2019EBP3175

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1462 / 1469

页数：8

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