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.

翻译：摘要：AI原生架构对于6G无线通信至关重要。在信道估计等关键应用中，深度学习模型的黑箱特性与高复杂度限制了其实际部署。尽管基于扰动的可解释人工智能（XAI）方案能够实现输入过滤，但往往忽视了内部结构优化。我们提出X-REFINE，一种基于XAI的联合输入过滤与结构微调框架。通过采用基于分解的符号稳定LRP-ε规则，X-REFINE反向传播预测结果，为子载波和隐藏神经元生成高分辨率相关性得分。该方法能够实现可靠优化，识别出最稳健的模型组件。仿真结果表明，与外部扰动型XAI框架相比，X-REFINE在性能-复杂度-可解释性之间实现了更优的平衡，在显著降低计算复杂度的同时保持了稳健的误码率（BER）性能。