基于概念瓶颈模型的可解释神经网络调制分类
Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models
摘要：
虽然RFML有望成为未来无线标准的关键推动者，但RFML技术的广泛采用面临的一个重大挑战是深度学习模型缺乏可解释性。这项工作探讨了使用CB模型作为一种手段，提供内在的决策解释的背景下，DL为基础的AMC。结果表明，所提出的方法不仅满足性能的单网络DL为基础的AMC算法，但提供了所需的模型的可解释性，并显示出潜在的分类调制方案没有看到在训练过程中（即zero-shot学习）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
