通过可解释性分析，实现车联网信道估计的高效压缩与泛化提升。
REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation
- 基于梯度的双层可解释框架，定位关键时频特征与通用内部表示
- 压缩后模型性能损失小于1 dB NMSE，且分布外泛化能力下降更慢
- 适合需要轻量化部署且注重鲁棒性的车载通信系统设计者
多通道混合信噪比训练能提升深度学习信道估计算法在IEEE 802.11p车联网通信中的分布外（OOD）泛化能力，但其内在机制尚不明确。本文提出REACH（基于相关性解释与架构压缩的信道估计算法），一个基于梯度的可解释性框架，分两个层次运行：输入级归因识别出在所有评估信道条件下均一致相关的时频特征子集，实现输入维度降维而性能损失极小；滤波器级归因揭示了一种近乎通用的内部表征，为观测到的分布外泛化提供了表征层面的解释。基于所得滤波器分类体系，引导的架构压缩显著降低参数量与浮点运算量（FLOPs），NMSE退化低于1 dB，且在持续压缩下分布外泛化能力优于域内精度下降速度。
原文摘要 · Abstract (English)
Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.11p vehicular communications, yet the internal mechanism responsible for this remains unexplained. This work presents REACH (Relevance-based Explanation and Architectural Compression for cHannel estimators), a gradient-based interpretability framework that operates at two levels. Input-level attribution identifies a subset of time-frequency features consistently relevant across all evaluated channel conditions, enabling input dimensionality reduction with minimal performance loss. Filter-level attribution reveals a near-universal internal representation, providing a representational account of the observed OOD generalisation. Guided by the resulting filter taxonomy, relevance-guided architecture compression substantially reduces both the number of parameters and the number of floating-point operations (FLOPs) with sub-1 dB normalised mean square error (NMSE) degradation, and OOD generalisation degrades more slowly than within-distribution accuracy under increasing compression.
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