基于循环谱能量的自适应频谱检测算法

张昊晔; 包志华; 张士兵

doi:1000-436X(2011)11-0095-09

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基于循环谱能量的自适应频谱检测算法

学术论文 | 更新时间：2024-06-05

- 基于循环谱能量的自适应频谱检测算法
- Adaptive spectrum sensing algorithm based on cyclostationary spectrum energy
- 通信学报 2011年32卷第11期页码：95-103
- 作者机构：
- 作者简介：
  
  [ "张昊晔（1987-），男，江苏南通人，南通大学硕士生，主要研究方向为通信信号处理、认知无线网络。" ]
  [ "包志华（1955-），男，江苏南通人，硕士，南通大学教授,主要研究方向为现代通信理论与技术、通信专用集成电路设计等。" ]
  [ "张士兵（1962-），男，江苏海门人，博士，南通大学教授，主要研究方向为超宽带无线通信、认知无线电网络。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(60872002);国家自然科学基金资助项目;The National Natural Science Foundation of China(61071086)
- DOI：1000-436X(2011)11-0095-09
  中图分类号： TN911.23
- 网络出版日期：2011-11，
  
  纸质出版日期：2011-11-25
- 稿件说明：
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张昊晔, 包志华, 张士兵. 基于循环谱能量的自适应频谱检测算法[J]. 通信学报, 2011,32(11):95-103.

Hao-ye ZHANG, Zhi-hua BAO, Shi-bing ZHANG. Adaptive spectrum sensing algorithm based on cyclostationary spectrum energy[J]. Journal on communications, 2011, 32(11): 95-103.
张昊晔, 包志华, 张士兵. 基于循环谱能量的自适应频谱检测算法[J]. 通信学报, 2011,32(11):95-103. DOI： 1000-436X(2011)11-0095-09.

Hao-ye ZHANG, Zhi-hua BAO, Shi-bing ZHANG. Adaptive spectrum sensing algorithm based on cyclostationary spectrum energy[J]. Journal on communications, 2011, 32(11): 95-103. DOI： 1000-436X(2011)11-0095-09.

摘要

根据信号循环平稳谱的特征，研究在低信噪比环境下的频谱检测问题，提出一种基于循环谱能量的自适应判决门限频谱检测算法。该算法融合能量检测与循环平稳特征检测的机理，以信号的循环谱能量为检测统计量，加权合并虚警率与检测率，准确估计循环谱特征值，构建了具有噪声自适应能力的频谱检测判决门限。仿真结果表明，该算法可以在低信噪比环境下有效地完成频谱检测，克服了噪声波动对频谱检测性能的影响，对不同调制主信号的感知具有稳健性。与最大—最小特征值算法和盲检测算法相比，该算法分别改善了信噪比4dB和8dB。

Abstract

Focused on the characteristic of cyclostationary spectrum of signal and the spectrum sensing in low signal-to-noise ratio

an adaptive decision threshold spectrum sensing algorithm was proposed

which combines cyclostationary spectrum sensing with energy detection.By weighting the sum of probabilities of detection and false alarm

estimating the eigenvalues of the cyclostationary spectrum

the algorithm provides an adaptive decision threshold which could differentiate the primary signal from the background noise.The simulation results show that the algorithm could perform well in low signal-to-noise ratio and remove the effect of noise uncertainty on the spectrum sensing.It outperforms maximum-minimum eigenvalue and blind detection with 4dB and 8dB respectively in signal-to-noise ratio

and has robustness to different modulated primary signal.

关键词

Keywords

references

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