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针对自动调制识别(Automatic Modulation Recognition, AMR)在实际应用中受限于小样本数据、时频多模态信息融合不充分，进而导致识别准确率较低的问题，对AMR领域现有技术的局限性进行了简要分析，提出了一种融合扩散模型与对比学习机制的跨模态自监督学习框架。该框架通过引入扩散模型，利用其生成能力实现通信信号高质量数据合成与增强，有效缓解小样本数据对模型训练的约束；同时结合跨模态对比学习机制，构建模态间关联学习模块，充分挖掘和利用时频不同模态表示之间的内在关联与互补信息，解决多模态信息融合不充分的痛点，最终基于上述设计构建了“扩散-对比混合网络(Diffusion-Contrastive Hybrid Network, DCHN)”模型。实验结果显示，该模型在RML2016.10a数据集上的识别准确率较其他网络模型有较大提升，具备较好的识别能力。
Abstract：To solve the problem that Automatic Modulation Recognition(AMR) is limited by small-sample data and insufficient fusion of time-frequency multimodal information in practical applications, which in turn leads to low recognition accuracy, the limitations of existing technologies in the AMR field are analyzed and a cross-modal self-supervised learning framework integrating a diffusion model and a contrastive learning mechanism is proposed. By introducing the diffusion model, the framework leverages its generative capability to achieve high-quality data synthesis and augmentation of communication signals, effectively alleviating the constraints of small-sample data on model training. Meanwhile, combined with the cross-modal contrastive learning mechanism, it constructs an inter-modal association learning module to fully explore and utilize the inherent correlations and complementary information between different time-frequency modal representations, thus solving the problem of insufficient multimodal information fusion. Finally, based on the above design, a Diffusion-Contrastive Hybrid Network(DCHN) model is established. Experimental results show that the recognition accuracy of this model on the RML2016.10a dataset is significantly higher than that of other network models, indicating that it possesses excellent recognition capability.
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基本信息:
中图分类号:TN911.3
引用信息:
[1]薄丹,王凯,刘云升,等.基于时频特征融合的自动调制识别方法[J].无线电工程,2025,55(11):2163-2173.
基金信息:
国家自然科学基金(62361048); 内蒙古自治区重点研发与成果转化计划项目(2023YFHH0069)~~