Significant Cycle Frequency based Feature Detection for Cognitive Radio Systems

被引：0

作者：

Shen Da ^{[1
]}

Gan Xiaoying ^{[1
]}

Chen Hsiao-Hwa ^{[2
]}

Qian Liang ^{[1
]}

Xu Miao ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China

[2] Natl Cheng Kung Univ, Dept Engn Sci, Tainan, Taiwan

来源：

2009 4TH INTERNATIONAL CONFERENCE ON COGNITIVE RADIO ORIENTED WIRELESS NETWORKS AND COMMUNICATIONS | 2009年

关键词：

Cognitive radio; cycle frequency; cyclostationary detection; energy detection;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In cognitive radio systems, one of the main requirements is to detect the presence of the primary users' transmission, especially in weak signal cases. Cyclostationary detection is always used to solve weak signal detection, however, the computational complexity prevents it from wide usage. In this paper, a significant cycle frequency based feature detection algorithm has been proposed, in which only cycle frequency with significant cyclic cumulant is considered for a certain modulation mode. The proposed algorithm greatly reduces the computation complexity for cyclic feature detection. Simulation results show that the proposed algorithm has a remarkable performance gain than energy detection when supporting fast detection with low computational complexity.

引用

页码：267 / +

页数：2

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