Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

TL;DR

This paper introduces TLDNN, a hybrid deep learning framework combining transformers and LSTMs for improved global feature extraction in automatic modulation recognition, achieving state-of-the-art results and robustness.

Contribution

The paper proposes a novel hybrid model TLDNN that integrates transformer self-attention and LSTM for better global and temporal feature extraction in AMR.

Findings

TLDNN outperforms existing methods on standard datasets.

Data augmentation via segment substitution improves model robustness.

The framework is effective in few-shot learning scenarios.

Abstract

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 \url{https://github.com/AMR-Master/TLDNN}.