标题： 在有限标签下通过重建驱动的视觉变压器增强自动调制识别 显示英文标题

标题： Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels

摘要： 自动调制识别（AMR）对于认知无线电、频谱监测和安全无线通信至关重要。 然而，现有的解决方案通常依赖于大型标记数据集或多阶段训练流程，这在实践中限制了可扩展性和泛化能力。 我们提出了一种统一的视觉变压器（ViT）框架，该框架集成了监督、自监督和重建目标。 该模型结合了ViT编码器、轻量级卷积解码器和线性分类器；重建分支将增强的信号映射回原始信号，将编码器锚定到细粒度的I/Q结构。 这种策略在预训练期间促进了鲁棒且具有区分性的特征学习，而在微调中的部分标签监督使得在有限标签下能够有效分类。 在RML2018.01A数据集上，我们的方法在低标签情况下优于监督的CNN和ViT基线，在仅使用15-20%标记数据时接近ResNet级别的准确率，并在不同信噪比（SNR）水平下保持强大的性能。 总体而言，该框架为AMR提供了一个简单、可泛化且标签高效的解决方案。 显示英文摘要

摘要： Automatic modulation recognition (AMR) is critical for cognitive radio, spectrum monitoring, and secure wireless communication. However, existing solutions often rely on large labeled datasets or multi-stage training pipelines, which limit scalability and generalization in practice. We propose a unified Vision Transformer (ViT) framework that integrates supervised, self-supervised, and reconstruction objectives. The model combines a ViT encoder, a lightweight convolutional decoder, and a linear classifier; the reconstruction branch maps augmented signals back to their originals, anchoring the encoder to fine-grained I/Q structure. This strategy promotes robust, discriminative feature learning during pretraining, while partial label supervision in fine-tuning enables effective classification with limited labels. On the RML2018.01A dataset, our approach outperforms supervised CNN and ViT baselines in low-label regimes, approaches ResNet-level accuracy with only 15-20% labeled data, and maintains strong performance across varying SNR levels. Overall, the framework provides a simple, generalizable, and label-efficient solution for AMR.