X-REFINE 通过可解释性优化，让信道估计更高效可靠。
X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation
- 基于可解释AI的输入筛选与网络结构联合优化
- 显著降低计算复杂度，保持稳定误码率性能
- 适合6G通信中对可解释性要求高的场景
面向6G无线通信的AI原生架构至关重要。深度学习模型在信道估计等关键应用中存在黑箱特性与高复杂度问题，限制了实际部署。现有基于扰动的可解释人工智能（XAI）方法虽能实现输入过滤，但忽略内部结构优化。本文提出X-REFINE框架，融合输入筛选与网络结构微调。通过基于分解的、符号稳定的LRP epsilon规则，将预测结果反向传播，生成子载波与隐藏神经元的高分辨率相关性评分，从而可靠识别关键模型组件。仿真结果表明，相比外部扰动型XAI框架，X-REFINE实现了更优的性能-复杂度-可解释性权衡，在显著降低计算复杂度的同时，保持了稳健的误码率（BER）表现。
原文摘要 · Abstract (English)
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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