Uncertainty-Aware Incremental Automatic Modulation Classification With Bayesian Neural Network

Recent advances in deep learning (DL) have significantly enhanced automatic modulation classification (AMC), reducing the dependence on intricate feature engineering and substantially improving classification accuracy. Nonetheless, the conventional DL-based AMC methods have mostly adopted frequentist approaches, often exhibit limited adaptability and struggle to offer reliable uncertainty estimates for their predictions, especially when trained on limited data. To address these challenges, we propose a novel Bayesian DL framework tailored for the uncertainty-aware incremental AMC. Our framework involves a two-stage process, beginning with the optimization of model parameters using a cumulative loss function in frequentist approach, followed by the application of Bayesian neural network (BNN) techniques, such as Laplace approximation and variational inference, for statistical inference. This framework ensures robust calibration and scalability, enabling the classifier to incrementally refine its predictions with the reception of additional signal samples and to terminate the refinement process once the confidence score meets the predetermined classification accuracy target. In addition, these enhanced capabilities of our classifier can extend to identifying a new, unseen modulation scheme by discerning distinct confidence score patterns associated with varying sequence lengths. Simulation results validate the efficacy of the proposed uncertainty-aware incremental AMC by showing enhanced classification accuracy and superior calibration relative to conventional DL-based AMC methods in practical scenarios with limited training data.