# Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

arXiv: https://arxiv.org/abs/2101.01239 · HTML: https://mlsanity.com/paper/2101.01239/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Schema Inference for Interpretable Image Classification](https://mlsanity.com/paper/2303.06635/) (2023)
- [DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks](https://mlsanity.com/paper/2407.14509/) (2024)
- [Rethinking Distance Metrics for Counterfactual Explainability](https://mlsanity.com/paper/2410.14522/) (2024)
- [Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network](https://mlsanity.com/paper/2507.03315/) (2025)

## Abstract (arXiv)

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).