Automatic Modulation Recognition Based on Adaptive Wavelet Enhancement and Dynamic Graph Construction

Automatic modulation recognition (AMR) is essential for cognitive radio and intelligent wireless communication, yet its performance degrades markedly under low signal-to-noise ratio (SNR) conditions because weak local structures are corrupted and class boundaries become less separable. To address this problem, we propose ADGNet, an automatic modulation recognition network based on adaptive wavelet enhancement and dynamic graph construction. ADGNet adopts a dual-branch architecture in which the main branch extracts time-domain amplitude–phase features from raw in-phase/quadrature sequences, while the auxiliary branch combines short-time Fourier transform features with adaptive wavelet features to capture complementary frequency–energy distributions and multi-scale transient details. A dual-domain adaptive encoder then recalibrates and fuses the two branches, suppressing redundant and noise-contaminated responses. In addition, a G2 dynamic topology module constructs sample-adaptive top-k adjacency matrices from node features and fuses them with the original graph structure to improve temporal relation modeling. Experiments on RadioML2016.10a and RadioML2016.10b show average accuracies of 64.23% and 69.69%, respectively. On RadioML2016.10b, ADGNet achieves 63.48% average accuracy from −12 dB to 0 dB, compared with 59.51% for the baseline. With approximately 0.13 million parameters, ADGNet provides a favorable balance between low-SNR robustness and model complexity.