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

Abstract

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）

CLC Number:

TN 92
TP 181

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.

Figures/Tables 11

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References 30

1	YUAN Y Z, XU J, LIU L Y. Correlation-based maximal invariant statistic for spectrum sensing[J]. IEEE Communications Letters, 2023, 27 (5): 1402- 1406. doi: 10.1109/LCOMM.2023.3250243
2	CHOUHAN A, CAPTAIN K, PARMAR A, et al. Defending cooperative spectrum sensing from byzantine attacks: an effective entropy-based weighted algorithm[J]. IEEE Wireless Communications Letters, 2023, 12 (12): 2063- 2067. doi: 10.1109/LWC.2023.3306814
3	ZHANG Q Y, ZENG W H, QIN Z J, et al. TaP2-CSS: a trustworthy and privacy-preserving cooperative spectrum sensing solution based on blockchain[J]. IEEE Internet of Things Journal, 2024, 11 (8): 14634- 14646. doi: 10.1109/JIOT.2023.3344648
4	ZHENG M, CHEN L, LIANG W, et al. Energy-efficiency maximization for cooperative spectrum sensing in cognitive sensor networks[J]. IEEE Trans. on Green Communications and Networking, 2017, 1 (1): 29- 39. doi: 10.1109/TGCN.2016.2646819
5	XIE S L, LIU Y, ZHANG Y, et al. A parallel cooperative spectrum sensing in cognitive radio networks[J]. IEEE Trans. on Vehicular Technology, 2010, 59 (8): 4079- 4092. doi: 10.1109/TVT.2010.2056943
6	CHEN Y, ZHAO Q, SWAMI A. Distributed spectrum sensing and access in cognitive radio networks with energy constraint[J]. IEEE Trans. on Signal Processing, 2009, 57 (2): 783- 797. doi: 10.1109/TSP.2008.2007928
7	MA S W, WANG Y H, REN J X, et al. A cooperative spectrum sensing method based on soft low-rank subspace clustering[J]. IEEE Trans. on Circuits and Systems II: Express Briefs, 2022, 69 (9): 3954- 3958.
8	MOAWAD A, YAO K C, MANSOUR A, et al. Spectrum sensing by cepstral covariance detection[J]. IEEE Communications Letters, 2022, 26 (6): 1323- 1327. doi: 10.1109/LCOMM.2022.3157773
9	KUMAR A, SAHA S, BHATTACHARYA R. Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs[J]. AEU-International Journal of Electronics and Communications, 2018, 84, 100- 110.
10	FAWZI A, EL-SHAFAI W, ABD-ELNABY M, et al. Adaptive two-stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems[J]. International Journal of Communication Systems, 2020, 33 (16): e4400. doi: 10.1002/dac.4400
11	申滨, 王欣, 陈思吉, 等. 基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知[J]. 电子与信息学报, 2021, 43 (1): 92- 100.
	SHEN B, WANG X, CHEN S J, et al. Machine learning based primary user transmit mode classification for spectrum sensing in cellular cognitive radio network[J]. Journal of Electronics & Information Technology, 2021, 43 (1): 92- 100.
12	周金, 李玉芝, 李斌. 小样本图像处理方法赋能的宽带频谱感知[J]. 电子与信息学报, 2023, 45 (3): 1102- 1110.
	ZHOU J, LI Y Z, LI B. Image processing-driven spectrum sensing with small training samples[J]. Journal of Electronics & Information Technology, 2023, 45 (3): 1102- 1110.
13	RADHI A A, ABDULLAH H N, AKKAR H A R. Denoised Jarque-Bera features-based K-Means algorithm for intelligent cooperative spectrum sensing[J]. Digital Signal Processing: A Review Journal, 2022, 129, 103659. doi: 10.1016/j.dsp.2022.103659
14	RADHI A A, AKKAR H A R, ABDULLAH H N. Skewness and access kurtosis as denoised mixed features-based K-Medoids for cooperative spectrum sensing[J]. Physical Communication, 2022, 54, 101831. doi: 10.1016/j.phycom.2022.101831
15	LIU H, ZHU X, FUJII T. A new classification-like scheme for spectrum sensing using spectral correlation and stacked denoising autoencoders[J]. IEICE Transactions on Communications, 2018, E101.B (11): 2348- 2361. doi: 10.1587/transcom.2017EBP3447
16	AKAY B, KARABOGA D, AKAY R. A comprehensive survey on optimizing deep learning models by metaheuristics[J]. Artificial Intelligence Review, 2022, 55 (2): 829- 894. doi: 10.1007/s10462-021-09992-0
17	王磊, 张劲, 叶秋炫. LDACS系统基于循环谱和残差神经网络的频谱感知方法[J]. 系统工程与电子技术, 2024, 46 (9): 3231- 3238.
	WANG L, ZHANG J, YE Q X. Spectrum sensing method based on cyclic spectrum and residual network in LDACS[J]. Systems Engineering and Electronics, 2024, 46 (9): 3231- 3238.
18	HUANG Y C, ZHANG C, PAN C Y. Spectrum sensing for DTMB-A system using accumulated autocorrelation[J]. IEEE Access, 2022, 10, 113610- 113618. doi: 10.1109/ACCESS.2022.3217496
19	SHI Y F, YANG C, WANG J, et al. A near-real-time forecasting model of high-frequency radiowave propagation factor fusion based on the ICEEMDAN decomposition and Bi-LSTM methods[J]. IEEE Trans. on Antennas and Propagation, 2024, 72 (7): 6032- 6044. doi: 10.1109/TAP.2024.3413298
20	LI X, LI H, YANG Z, et al. Radiation signal denoising method of loaded coal-rock based on ICEEMDAN-PCK-Means-IP[J]. IEEE Sensors Journal, 2023, 23 (19): 23103- 23118. doi: 10.1109/JSEN.2023.3306932
21	TANG L H, ZHAO L, JIANG Y. An SVM-based feature detection scheme for spatial spectrum sensing[J]. IEEE Communications Letters, 2023, 27 (8): 2132- 2136. doi: 10.1109/LCOMM.2023.3289982
22	HEIDARI A A, MIRJALILI S, FARIS H, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Systems, 2019, 97, 849- 872. doi: 10.1016/j.future.2019.02.028
23	CHEN L, FENG C, MA Y. Improved harris hawks optimization for global optimization and engineering design[J]. Cluster Computing, 2024, 27 (2): 2003- 2027. doi: 10.1007/s10586-023-04020-y
24	MIRRASHID M, NADERPOUR H. Transit search: an optimization algorithm based on exoplanet exploration[J]. Results in Control and Optimization, 2022, 7, 100127. doi: 10.1016/j.rico.2022.100127
25	SHEHAB M, MASHAL I, MOMANI Z, et al. Harris hawks optimization algorithm: variants and applications[J]. Archives of Computational Methods in Engineering, 2022, 29 (7): 5579- 5603. doi: 10.1007/s11831-022-09780-1
26	JAKOB J, GROSS M, GUNTHER T. A fluid flow data set for machine learning and its application to neural flow map interpolation[J]. IEEE Trans. on Visualization and Computer Graphics, 2021, 27 (2): 1279- 1289. doi: 10.1109/TVCG.2020.3028947
27	CAO J W, FENG Y M, ZHENG R Z, et al. Two-stream attention 3-D deep network-based childhood epilepsy syndrome classification[J]. IEEE Trans. on Instrumentation and Measurement, 2023, 72, 2503412.
28	SABER M, EL RHARRAS A, SAADANE R, et al. Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms[J]. Procedia Computer Science, 2020, 176, 2404- 2413. doi: 10.1016/j.procs.2020.09.311
29	TAVARES C H A, MARINELLO J C, PROENCA Jr M L, et al. Machine learning-based models for spectrum sensing in cooperative radio networks[J]. IET Communications, 2020, 14 (18): 3102- 3109. doi: 10.1049/iet-com.2019.0941
30	YAKKATI R R, YAKKATI R R, TRIPATHY R K, et al. Radio frequency spectrum sensing by automatic modulation classification in cognitive radio system using multiscale deep CNN[J]. IEEE Sensors Journal, 2022, 22 (1): 926- 938. doi: 10.1109/JSEN.2021.3128395

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

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