Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

Lauren J. Wong; Sean McPherson

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arXiv · 2101.01239

Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

Lauren J. Wong·Sean McPherson

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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).

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BibTeX RIS

Lauren J. Wong, Sean McPherson. 2021-01-04. Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models. https://arxiv.org/abs/2101.01239

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