REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation
arXiv:2606.11857v1 Announce Type: cross 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, ena
The increasing complexity and opacity of deep learning models for critical applications like vehicular communications necessitates robust interpretability frameworks to ensure reliability and trust.
This work directly addresses the 'black box' problem in AI, offering a method to understand, optimize, and compress models for safety-critical systems, which is crucial for autonomous technologies.
The ability to identify key features and compress models based on interpretability will lead to more robust, efficient, and trustworthy AI deployments in sensitive areas like vehicular communication.
- · Autonomous vehicle developers
- · AI interpretability researchers
- · Deep learning hardware manufacturers
- · Telecommunications infrastructure providers
- · Developers of uninterpretable, large models for critical applications
- · Companies relying solely on 'black box' AI solutions for vehicular tech
Improved reliability and safety metrics for AI-driven vehicular communication systems.
Accelerated adoption of interpretable AI in other safety-critical domains beyond vehicular tech, such as healthcare and industrial control.
Potential for new regulatory frameworks and industry standards mandating interpretability and compression metrics for AI in high-stakes applications.
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Read at arXiv cs.LG