  While 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 CB
models as a means to provide inherent decision explanations in the context of
DL-based 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).
