基于自相关函数图特征的频谱感知算法研究

胡国兵; 赵敦博; 杨莉; 赵嫔姣

doi:10.12263/DZXB.20221002

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基于自相关函数图特征的频谱感知算法研究

学术论文 | 更新时间：2025-12-08

- 基于自相关函数图特征的频谱感知算法研究
- Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation
- 电子学报 2023年51卷第5期页码：1327-1333
- 作者机构：
  
  1.金陵科技学院电子信息工程学院,江苏南京 211169
  2.南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院,江苏南京 210023
- 作者简介：
  
  [ "胡国兵男，1978年4月生，江苏高淳人.2011年于南京航空航天大学获工学博士学位，现为金陵科技学院电子信息工程学院教授、硕士生导师．主要从事智能信号处理等方面的研究. E-mail: s0304152@jit.edu.cn" ]
  [ "赵敦博男，1996年9月出生，江苏省宿迁市人，2022年在南京邮电大学获得电子与通信工程硕士学位.目前在5G通信领域，从事物理下行共享信道开发工作. E-mail: d_beau@163.com" ]
  [ "杨莉女，1983年3月生，江苏高淳人.2008年毕业于扬州大学电子信息工程学院，获工学硕士学位，现为金陵科技学院电子信息工程学院高级实验师，从事通信信号调制识别方面的研究. E-mail: yangli691@jit.edu.cn" ]
  [ "赵嫔姣女，1990年6月出生，黑龙江省哈尔滨市人.2017年在哈尔滨工程大学信息与通信工程专业获得博士学位.现为金陵科技学院电子信息工程学院副教授，主要研究方向为阵列信号处理、高精度无源测向. E-mail: zhaopinjiao@hrbeu.edu.cn" ]
- 基金信息：
  
  江苏省高等学校自然科学研究重大项目(20KJA510008);国家自然科学基金项目(62101223)
- DOI：10.12263/DZXB.20221002
  中图分类号： TN957.51
- 收稿：2022-09-02，
  
  修回：2022-12-14，
  
  纸质出版：2023-05-25
- 稿件说明：
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胡国兵,赵敦博,杨莉等.基于自相关函数图特征的频谱感知算法研究[J].电子学报,2023,51(05):1327-1333.

HU Guo-bing,ZHAO Dun-bo,YANG Li,et al.Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation[J].ACTA ELECTRONICA SINICA,2023,51(05):1327-1333.
胡国兵,赵敦博,杨莉等.基于自相关函数图特征的频谱感知算法研究[J].电子学报,2023,51(05):1327-1333. DOI： 10.12263/DZXB.20221002.

HU Guo-bing,ZHAO Dun-bo,YANG Li,et al.Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation[J].ACTA ELECTRONICA SINICA,2023,51(05):1327-1333. DOI： 10.12263/DZXB.20221002.

摘要

现有图域频谱感知算法主要借助于完全图检测，其性能在低信噪比时不佳.为此，本文提出了一种基于自相关函数图域变换的感知算法，可有效改善低信噪比下的检测性能.其基本思路为：将去均值后观测信号的自相关函数通过归一化、量化等环节转换到图域，在分析图连通性差异的基础上，将图拉普拉斯阵的零特征值个数作为检验统计量，以完成对频谱的有效感知.文中利用受控不等式理论阐明了随机序列分布的随机性、样本数及量化级数与图的连通性之间的相互关系.仿真结果表明，在信噪比为-10 dB时，本文算法的检测概率接近100%，其性能优于现有图域感知算法，且计算复杂度适中，具有较好的应用效能.

Abstract

The conventional graph domain-based spectrum sensing algorithms are mainly depended on checking for the completeness of the graphs

and their performances deteriorate at low signal-to-noise ratio (SNR). Therefore

this paper proposes a spectrum sensing algorithm in cognitive radio (CR) based on the graph domain transformation of the autocorrelation function

which can efficiently improve the performance of detection. Firstly

the autocorrelation function of the observed signal after removing its mean value is converted to the graph domain. Then

based on analyzing the difference in the number of connected components of generated graphs

the number of zero eigenvalues of the graph Laplacian matrix is used as the test statistic to complete the sensing of CR spectrum. Moreover

the relationships between the randomness of random sequence distribution

the number of samples

the number of the quantization levels and the connectivity of the graph are proved using the theory of majorization inequality. The simulation results show that when the SNR is -10 dB

the detection probability of the algorithm is close to 100%

and its performance is better than the existing graph-based sensing algorithms with moderate computational complexity

which accordingly acquired an superior efficacy.

关键词

Keywords

references

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