A Dual-Branch Network With Feature Assistance for Automatic Modulation Recognition

作     者：Feng, Yuhang Duan, Ruifeng Li, Shurui Cheng, Peng Liu, Wanchun 

作者机构：Beijing Forestry Univ Sch Informat Sci & Technol Beijing 100083 Peoples R China Beijing Forestry Univ Sch Artificial Intelligence Beijing 100083 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Univ Sydney Sydney NSW 2006 Australia Univ Sydney Sch Elect Enginnering & Comp Sci Camperdown NSW 2308 Australia 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2025年第32卷

页      面：701-705页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Beijing Natural Science Foundation [L202003] China Scholarship Council 

主　　题：Feature extraction Transformers Modulation Correlation Convolution Accuracy Encoding Data mining Australia Training Automatic modulation recognition gramian angular field depthwise separable convolution transformer dual-branch network 

摘      要：Automatic modulation recognition (AMR) is a critical technology in wireless communications, aiming to achieve high recognition accuracy with low complexity in increasingly intricate electromagnetic environments. To tackle this challenge, in this paper, we propose a dual-branch convolution cascaded transformer network with feature assistance, termed DCTFANet. To enhance the differentiation between samples, we employ the gramian angular field (GAF) to capture potential temporal correlations between each data point. Subsequently, both I/Q sequences and GAF data are input into the model for joint signal feature extraction. The network backbone is constructed using multiple improved depthwise separable convolution (DSC) blocks, which significantly reduce computational complexity. Moreover, the backbone depth is flexibly adjustable to fully exploit local features of different data types. Finally, feature transition and the transformer encoder are used to reduce parameters and extract global feature. Experimental results on RML2016.10b show that the proposed method achieves higher recognition accuracy compared to several state-of-the-art methods, especially at low signal-to-noise ratios (SNRs), with an increase of at least 10.80% at -20 dB.