Automatic Modulation Recognition (AMR) plays a crucial role in wireless
communication systems. Deep learning AMR strategies have achieved tremendous
success in recent years. Modulated signals exhibit long temporal dependencies,
and extracting global features is crucial in identifying modulation schemes.
Traditionally, human experts analyze patterns in constellation diagrams to
classify modulation schemes. Classical convolutional-based networks, due to
their limited receptive fields, excel at extracting local features but struggle
to capture global relationships. To address this limitation, we introduce a
novel hybrid deep framework named TLDNN, which incorporates the architectures
of the transformer and long short-term memory (LSTM). We utilize the
self-attention mechanism of the transformer to model the global correlations in
signal sequences while employing LSTM to enhance the capture of temporal
dependencies. To mitigate the impact like RF fingerprint features and channel
characteristics on model generalization, we propose data augmentation
strategies known as segment substitution (SS) to enhance the model's robustness
to modulation-related features. Experimental results on widely-used datasets
demonstrate that our method achieves state-of-the-art performance and exhibits
significant advantages in terms of complexity. Our proposed framework serves as
a foundational backbone that can be extended to different datasets. We have
verified the effectiveness of our augmentation approach in enhancing the
generalization of the models, particularly in few-shot scenarios. Code is
available at https://github.com/AMR-Master/TLDNN.

自动调制识别在无线通信系统中起着关键作用，我们提出了一种名为 TLDNN 的混合深度框架，将 Transformer 和 LSTM 的结构相结合，通过自注意机制建模信号序列的全局相关性以增强时间依赖性的捕捉，并采用段替换（SS）的数据增强策略来提高模型对调制相关特征的鲁棒性，实验结果表明我们的方法在广泛使用的数据集上取得了最先进的性能并在复杂性方面具有显著优势。