Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

TL;DR

This paper introduces concept bottleneck models to enhance explainability in deep learning-based automatic modulation classification, achieving comparable performance to traditional methods while providing interpretability and potential zero-shot classification capabilities.

Contribution

It proposes using concept bottleneck models for inherently explainable modulation classification, a novel approach in RFML.

Findings

Achieves performance comparable to standard deep learning models.

Provides inherent explainability in modulation classification.

Shows potential for zero-shot learning of unseen modulation schemes.

Abstract

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).