REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation
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Abstract (arXiv)
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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References · 12
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
- 2016Pruning Filters for Efficient ConvNetscited by 1,143
- 2018Soft Filter Pruning for Accelerating Deep Convolutional Neural Networkscited by 315
- 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 202
- 2023Structured Pruning for Deep Convolutional Neural Networks: A surveycited by 122
- 2019Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruningcited by 89
- 2018Deep Learning-Based Channel Estimationcited by 75
- 2023Towards Explainable AI for Channel Estimation in Wireless Communicationscited by 5
- 2021Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11p Standardcited by 3
- 2023RNN Based Channel Estimation in Doubly Selective Environmentscited by 3
- 2025A Data Pilot-Aided Temporal Convolutional Network for Channel Estimation in IEEE 802.11p Vehicle-to-Vehicle Communicationscited by 1
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