Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

While Radio Frequency Machine Learning (RFML) is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep learning models. This work investigates the use of Concept Bottleneck (CB) models as a means to provide inherent decision explanations in the context of deep learning (DL)-based Automatic Modulation Classification (AMC). Results show that the proposed approach not only meets the performance of single-network DL-based AMC algorithms, but provides the desired model explainability and shows potential for classifying modulation schemes not seen during training (i.e. zero-shot learning).