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arXiv · 2602.22277

X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

Abdul Karim Gizzini·Yahia Medjahdi
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Abstract

AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based eXplainable Artificial Intelligence (XAI) solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons. This enables a reliable optimization that identifies the most reliable model components. Simulation results demonstrate that X-REFINE achieves a superior performance-complexity-interpretability trade-off compared to the external perturbation-based XAI frameworks, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance.

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Abdul Karim Gizzini, Yahia Medjahdi. 2026-02-25. X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation. https://arxiv.org/abs/2602.22277

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