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While RFML is expected to be a key enabler of future wireless standards, asignificant challenge to the widespread adoption of RFML techniques is the lackof explainability in deep learning models. This work investigates the use of CBmodels as a means to provide inherent decision explanations in the context ofDL-based AMC. Results show that the proposed approach not only meets theperformance of single-network DL-based AMC algorithms, but provides the desiredmodel explainability and shows potential for classifying modulation schemes notseen during training (i.e. zero-shot learning).

Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models