REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation

• URL: http://arxiv.org/abs/2606.11857v1
• Date: Wed, 10 Jun 2026 09:36:03 GMT
• Title: REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation
• Abstract summary: Multi-channel mixed-SNR training improves out-of-distribution generalisation of channel estimators for IEEE 802.11p vehicular communications.<n>This work presents REACH, a gradient-based interpretability framework that operates at two levels.
• Score: 2.466065249430993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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