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Enhancing Noise Robustness in Few-Shot Automatic Modulation Classification via Complex-Valued Autoencoders

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Titel: Enhancing Noise Robustness in Few-Shot Automatic Modulation Classification via Complex-Valued Autoencoders
Autoren: Minghui Gao, Binquan Zhang, Lu Wang, Xiaogang Tang, Hao Huan
Quelle: Electronics ; Volume 15 ; Issue 3 ; Pages: 674
Verlagsinformationen: Multidisciplinary Digital Publishing Institute
Publikationsjahr: 2026
Bestand: MDPI Open Access Publishing
Schlagwörter: automatic modulation classification, signal noise reduction, few-shot, complex-valued neural network
Beschreibung: The emergence of radio frequency machine learning has significantly propelled the application of deep learning (DL) methods in automatic modulation classification (AMC). However, under non-cooperative scenarios, the performance of DL-based AMC suffers severe performance degradation due to scarce labeled samples and noise interference. To enhance noise robustness in few-shot AMC, this paper proposes a complex-domain autoencoder-based method where a complex-valued noise reduction network (CNRN) is embedded into the AMC framework, jointly extracting complex-valued and temporal features from noisy signals to achieve signal–noise separation. Our framework executes four sequential operations: high-signal-to-noise-ratio (high-SNR) samples are first isolated from limited raw data via unsupervised classification; rotation and cyclic time-shifting operations then augment the sample space; the CNRN is subsequently trained on augmented data; and final AMC classification is implemented through DL-based classifiers. Experimental validation on RML 2016.10a dataset demonstrates: (1) for −20 dB signals, denoising achieves 20.18 dB SNR improvement with 87.74% mean squared error reduction; (2) across the −20 dB to 18 dB range, denoised signals exhibit accuracy improvements of 21.57% under DL-based classifiers. Physical validation further confirms that the proposed method exhibits enhanced noise robustness, demonstrating its practical utility in real-world scenarios.
Publikationsart: text
Dateibeschreibung: application/pdf
Sprache: English
Relation: https://dx.doi.org/10.3390/electronics15030674
DOI: 10.3390/electronics15030674
Verfügbarkeit: https://doi.org/10.3390/electronics15030674
Rights: https://creativecommons.org/licenses/by/4.0/
Dokumentencode: edsbas.883B351E
Datenbank: BASE
Beschreibung
Abstract:The emergence of radio frequency machine learning has significantly propelled the application of deep learning (DL) methods in automatic modulation classification (AMC). However, under non-cooperative scenarios, the performance of DL-based AMC suffers severe performance degradation due to scarce labeled samples and noise interference. To enhance noise robustness in few-shot AMC, this paper proposes a complex-domain autoencoder-based method where a complex-valued noise reduction network (CNRN) is embedded into the AMC framework, jointly extracting complex-valued and temporal features from noisy signals to achieve signal–noise separation. Our framework executes four sequential operations: high-signal-to-noise-ratio (high-SNR) samples are first isolated from limited raw data via unsupervised classification; rotation and cyclic time-shifting operations then augment the sample space; the CNRN is subsequently trained on augmented data; and final AMC classification is implemented through DL-based classifiers. Experimental validation on RML 2016.10a dataset demonstrates: (1) for −20 dB signals, denoising achieves 20.18 dB SNR improvement with 87.74% mean squared error reduction; (2) across the −20 dB to 18 dB range, denoised signals exhibit accuracy improvements of 21.57% under DL-based classifiers. Physical validation further confirms that the proposed method exhibits enhanced noise robustness, demonstrating its practical utility in real-world scenarios.
DOI:10.3390/electronics15030674