低信噪比下基于ICEEMDAN和HHO的协作频谱感知方法

doi:10.12305/j.issn.1001-506X.2025.09.33

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (9): 3109-3116.doi: 10.12305/j.issn.1001-506X.2025.09.33

• 通信与网络 • 上一篇

低信噪比下基于ICEEMDAN和HHO的协作频谱感知方法

王全全¹^,*, 谢松霖¹^,²(), 顾志豪¹, 吴城坤³, 张更新¹

1. 南京邮电大学通信与信息工程学院，江苏南京 210003
2. 南京邮电大学波特兰学院，江苏南京 210023
3. 国家无线电监测中心，北京 100037

收稿日期:2024-07-24 出版日期:2025-09-25 发布日期:2025-09-16
通讯作者: 王全全 E-mail:1023213130@njupt.edu.cn
作者简介:谢松霖（2001—），男，硕士研究生，主要研究方向为认知无线电频谱感知
顾志豪（1997—），男，硕士研究生，主要研究方向为认知无线电频谱感知
吴城坤（1998—），男，工程师，硕士，主要研究方向为无线电频谱管理与监测
张更新（1967—），男，教授，博士，主要研究方向为卫星通信、频谱监测与物联网
基金资助:
国家自然科学基金（U21A20450）；江苏省教育科学规划课题高校重点课题（B/2023/01/120）资助课题

Cooperative spectrum sensing method based on ICEEMDAN and HHO under low signal to noise ratio

Quanquan WANG¹^,*, Songlin XIE¹^,²(), Zhihao GU¹, Chengkun WU³, Gengxin ZHANG¹

1. School of Communication and Information Engineering，Nanjing University of Posts and Telecommunications，Nanjing 210003，China
2. Portland Institute，Nanjing University of Posts and Telecommunications，Nanjing 210023，China
3. State Radio Monitoring Center，Beijing 100037，China

Received:2024-07-24 Online:2025-09-25 Published:2025-09-16
Contact: Quanquan WANG E-mail:1023213130@njupt.edu.cn

摘要/Abstract

摘要：

为解决频谱感知在低信噪比下性能受限的问题，提出了一种基于改进的自适应噪声完备集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN）和哈里斯鹰优化（Harris hawks optimization，HHO）的协作频谱感知方法。首先为获得固有模态函数（intrinsic mode function，IMF）分量，对次用户上传信号进行ICEEMDAN处理，其次计算已知波形的主用户（primary user，PU）信号与各IMF分量之间的相关系数，然后提取合适的IMF分量累加得到重构信号。接着用重构信号的平均能量值作为特征值训练支持向量机（support vector machine，SVM），并通过HHO优化SVM参数，最后用优化后的SVM模型对PU是否存在进行检测。实验结果表明，所提方法在低信噪比下检测概率、检测准确率均较高，感知性能较好。

关键词: 协作频谱感知, 改进的自适应噪声完备集合经验模态分解, 降噪, 哈里斯鹰优化, 支持向量机

Abstract:

A cooperative spectrum sensing method based on improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and Harris hawks optimization （HHO） is proposed to solve the problem of constrained spectrum sensing performance under low signal to noise ratio. Firstly, the signal uploaded by secondary users is processed by ICEEMDAN to acquire the intrinsic mode function （IMF） components. Secondly, the correlation coefficient of the primary user （PU） signal of known waveform and each IMF component is calculated. Some IMF components with correlation coefficient above a threshold are selected and accumulated to derive the reconstructed signals. Thereafter, the average energy value of the reconstructed signals is used as the feature value to train the support vector machine （SVM） model, and the HHO is used to optimize the SVM parameters. Finally, the optimized SVM model is used to detect the presence of PU. The experimental results demonstrate that the proposed method has higher detection probability, accuracy, and better sensing performance under low signal to noise ratio.

Key words: cooperative spectrum sensing, improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）, noise reduction, Harris hawks optimization （HHO）, support vector machine （SVM）

中图分类号:

TN 92
TP 181

王全全, 谢松霖, 顾志豪, 吴城坤, 张更新. 低信噪比下基于ICEEMDAN和HHO的协作频谱感知方法[J]. 系统工程与电子技术, 2025, 47(9): 3109-3116.

Quanquan WANG, Songlin XIE, Zhihao GU, Chengkun WU, Gengxin ZHANG. Cooperative spectrum sensing method based on ICEEMDAN and HHO under low signal to noise ratio[J]. Systems Engineering and Electronics, 2025, 47(9): 3109-3116.

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逃脱率r_e与剩余能量E_rabbit	围攻方案
r_e≥0.5且\|E_rabbit\|≥0.5	软围攻
r_e≥0.5且\|E_rabbit\|<0.5	硬围攻
r_e<0.5且\|E_rabbit\|≥0.5	渐进快速俯冲式软围攻
r_e<0.5且\|E_rabbit\|<0.5	渐进快速俯冲式硬围攻

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低信噪比下基于ICEEMDAN和HHO的协作频谱感知方法

Cooperative spectrum sensing method based on ICEEMDAN and HHO under low signal to noise ratio

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