Transformer Model Embedding Dual Stream for Modulation Classification of Short Signal Samples

Automatic modulation classification (AMC) is a critical task in modern communication systems, particularly under diverse signal conditions and limited data scenarios. Existing transformer-based AMC models often rely on single-stream architectures and uniform input formats, which limit their effectiveness in capturing rich signal features. To address these limitations, we propose DTNet, a novel transformer-based dual-stream network designed for efficient and accurate modulation classification. DTNet introduces two key innovations: (1) a scale feature and extension (SFE) block that applies a scaling function to transform signals into a structured output map, followed by an extension module that reconstructs the signal into a square matrix and integrates a response map using adaptive filters; and (2) a convolutional stream that extracts discriminative features from multiscale signal representations. Furthermore, a modified feature embedding to leverage the transformer architecture is introduced to capture global dependencies and contextual information from the input signal, thereby enhancing the modulation classification accuracy. Experimental results show that DTNet achieves superior performance on benchmark datasets, reaching classification accuracies of 93.4% on RML2016.10A and 94.4% on RML2016.10B, outperforming state-of-the-art deep learning methods while maintaining lower computational complexity. The source code is available at https://github.com/daothanh2011/DTNet .