Automatic Modulation Recognition Based on RadioAlign Feature Adaptation, Scale Attention, and a Bayesian Residual Branch

Abstract

To address amplitude scaling, phase rotation, local misalignment, and ambiguous decision boundaries under low-SNR and non-cooperative reception conditions, we propose an Attention RadioAlign Bayesian Residual Network (ABRNet) for automatic modulation recognition. ABRNet takes raw I/Q sequences as input. It first employs a RadioAlign module to estimate and apply bounded learnable transformations that aim to mitigate amplitude-phase and local-offset input variations. A scale-attention module then adaptively weights short-term phase transitions, local waveform patterns, and long-term envelope variations. Finally, a Bayesian residual branch adds a small stochastic residual to the deterministic logits and estimates the predictive mean, variance, and entropy as reliability measures. Across five random seeds, ABRNet achieves a mean overall accuracy of 63.4668% (sample standard deviation: 0.0583%), a low-SNR accuracy of 34.2864% (0.1399%), a high-SNR accuracy of 92.6473% (0.0630%), and a Macro F1 score of 65.7086% (0.0784%). The error-detection AUROC is 0.894092 (0.000370), the expected calibration error is 0.016618 (0.001703), and the Brier score is 0.434423 (0.000127). With approximately 287 K parameters, ABRNet provides classification and predictive reliability measures in an end-to-end I/Q processing pipeline.