基于集成学习与特征降维的小样本调制识别方法

doi:10.12305/j.issn.1001-506X.2021.04.28

Abstract

Abstract:

Aiming at the problem of communication signal modulation recognition with few labeled samples, a few-shot modulation classification model based on ensemble learning and feature dimension reduction is proposed. Firstly, the feature set is formed by integrating handcrafted features and deep learning features. And then, the feature selection algorithm is designed to optimize the feature set to generate an optimal feature subset. Finally, the signals are distinguished by a fast convergent high-performance classifier, which can realize the modulation classification under the condition of a small number of labeled samples and a large number of unlabeled samples. The simulation results show that when the signal to noise ratio is 20 dB, the proposed algorithm can improve the signal recognition rate to 96% through modulation recognition of eight kinds of digital signals. At the same time, the algorithm is simple and has great application value.

Key words: modulation recognition, few-shot, ensemble learning, feature selection

CLC Number:

TN975

Yunhao SHI, Hua XU, Wanze ZHENG, Yinghui LIU. Few-shot modulation recognition method based on ensemble learning and feature dimension reduction[J]. Systems Engineering and Electronics, 2021, 43(4): 1099-1109.

Figures/Tables 15

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特征选择算法	所选特征数	特征子集	时间/s
mRMR	26	f₂、f₃、f₅、f₆、f₇、f₈、f₉、f₁₀、f₁₁、f₁₂、f₁₄、f₁₅、f₁₆、f₁₉、f₂₁、f₂₂、f₂₇、f₂₈、f₃₀、f₃₁、f₃₂、f₃₃、f₃₄、f₃₅、f₃₆、f₃₇	0.7
FCBF	19	f₃、f₆、f₇、f₈、f₉、f₁₀、f₁₂、f₁₄、f₁₅、f₁₆、f₁₉、f₂₁、f₂₇、f₂₈、f₃₀、f₃₂、f₃₄、f₃₆、f₃₇	1.3
SRFS	7	f₃、f₉、f₁₂、f₁₆、f₂₆、f₂₇、f₃₆	37.4
本文算法	13	f₃、f₇、f₈、f₉、f₁₀、f₁₂、f₁₆、f₁₉、f₂₁、f₂₇、f₃₂、f₃₆、f₃₇	9.8

分类器	最高识别准确率/%	训练时间/s
BP网络	96.0	7.04
XGBOOST	94.1	6.26
KNN	91.2	8.31

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Few-shot modulation recognition method based on ensemble learning and feature dimension reduction

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