Multi-Channel Physical Feature Convolution and Tri-Branch Fusion Network for Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays a critical role in intelligent wireless communication systems, particularly under conditions with a low signal-to-noise ratio (SNR) and complex channel environments. To address these challenges, this paper proposes a three-branch fusion network that integrates complementary features from the time, frequency, and spatial domains to enhance classification performance. The model consists of three specialized branches: a multi-channel convolutional branch designed to extract discriminative local features from multiple signal representations; a bidirectional long short-term memory (BiLSTM) branch capable of capturing long-range temporal dependencies; and a vision transformer (ViT) branch that processes constellation diagrams to exploit global structural information. To effectively merge these heterogeneous features, a path attention module is introduced to dynamically adjust the contribution of each branch, thereby achieving optimal feature fusion and improved recognition accuracy. Extensive experiments on the two popular benchmarks, RML2016.10a and RML2018.01a, show that the proposed model consistently outperforms baseline approaches. These results confirm the effectiveness and robustness of the proposed approach and highlight its potential for deployment in next-generation intelligent modulation recognition systems operating in realistic wireless communication environments.