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

• URL: http://arxiv.org/abs/2602.22277v1
• Date: Wed, 25 Feb 2026 10:20:26 GMT
• Title: X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation
• Authors: Abdul Karim Gizzini, Yahia Medjahdi,
• Abstract summary: We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning.<n>X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons.
• Score: 0.2578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 holistic optimization that identifies the most faithful model components. Simulation results demonstrate that X-REFINE achieves a superior interpretability-performance-complexity trade-off, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance across different scenarios.

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