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<p>Automatic modulation classification (AMC) serves as a fundamental task in non-cooperative communication systems, playing a crucial role in signal detection and demodulation. However, acquiring a sufficient number of high-quality labeled samples in real-world scenarios remains challenging, often resulting in insufficient training of end-to-end AMC networks and limited classification performance. To enhance the discrimination of easily confused modulation types and improve overall accuracy under few-shot conditions, this paper introduces a complex-valued circular convolutional meta multi-task network (CCC-MMTN). The proposed framework integrates meta-learning to extract discriminative features from limited data and adopts a multi-task strategy with an alternating local-global training mechanism to facilitate knowledge transfer and strengthen classification robustness. At its core, a complex-valued circular convolutional network (CCCN) is designed as the feature extractor to effectively capture temporal and complex-domain characteristics of communication signals. Extensive experiments on the RML2016.10a dataset demonstrate that, using only five samples per modulation type at a single signal-to-noise ratio (SNR) across eight modulation formats, our method achieves average accuracy rate of 77.35% (-20 dB to 18 dB). The method also achieves an overall accuracy of 84.09% through physical validation with 7 modulation types (50 samples per type) under random SNR conditions, confirming its real-world classification performance.</p>

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CCC-MMTN: towards robust classification of confusable modulations in few-shot scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月12日

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  • Minghui Gao,
  • Binquan Zhang,
  • Lu Wang,
  • Xiaogang Tang,
  • Hao Huan

摘要

Automatic modulation classification (AMC) serves as a fundamental task in non-cooperative communication systems, playing a crucial role in signal detection and demodulation. However, acquiring a sufficient number of high-quality labeled samples in real-world scenarios remains challenging, often resulting in insufficient training of end-to-end AMC networks and limited classification performance. To enhance the discrimination of easily confused modulation types and improve overall accuracy under few-shot conditions, this paper introduces a complex-valued circular convolutional meta multi-task network (CCC-MMTN). The proposed framework integrates meta-learning to extract discriminative features from limited data and adopts a multi-task strategy with an alternating local-global training mechanism to facilitate knowledge transfer and strengthen classification robustness. At its core, a complex-valued circular convolutional network (CCCN) is designed as the feature extractor to effectively capture temporal and complex-domain characteristics of communication signals. Extensive experiments on the RML2016.10a dataset demonstrate that, using only five samples per modulation type at a single signal-to-noise ratio (SNR) across eight modulation formats, our method achieves average accuracy rate of 77.35% (-20 dB to 18 dB). The method also achieves an overall accuracy of 84.09% through physical validation with 7 modulation types (50 samples per type) under random SNR conditions, confirming its real-world classification performance.