Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation

HU Guo-bing; ZHAO Dun-bo; YANG Li; ZHAO Pin-jiao

doi:10.12263/DZXB.20221002

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Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation

PAPERS | 更新时间：2025-12-08

- Research on Spectrum Sensing Based on Graphical Feature of the Autocorrelation
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1327-1333(2023)
- 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：
- 基金信息：
  
  Foundation Item(s):　Foundation Item(s): Key Natural Science Foundation of the Jiangsu Higher Education Institutions of China(20KJA510008);National Natural Science Foundation of China(62101223)
- DOI：10.12263/DZXB.20221002
  CLC： TN957.51
- Received：02 September 2022，
  
  Revised：2022-12-14，
  
  Published：25 May 2023
- 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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