X-RACE：用于信道估计的XAI辅助循环神经网络归因
X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation
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中文总结 AI 辅助
针对高移动性信道估计中LSTM黑盒与高开销问题，提出X-RACE框架，通过一次性双优化剪除冗余子载波与隐藏单元，并引入时间XAI指标，在降低推理复杂度至少44.1%的同时保持或改善误码率。
中文摘要 AI 辅助
深度学习模型，特别是长短期记忆网络（LSTM），在高移动性车载环境的信道估计中已展现出良好的性能。然而，其黑盒特性和架构开销限制了可信度和效率。经典的可解释人工智能（XAI）方法依赖昂贵的迭代过程，仅提供输入级过滤，而未解决架构微调问题。为克服这些局限，本文提出了XAI辅助循环神经网络归因用于信道估计（X-RACE）框架。X-RACE采用低复杂度的一次性双优化策略，同时评估并剪除不相关的输入子载波和内部隐藏单元。此外，我们提出了新颖的时间XAI指标：饱和时间、重要性漂移和相关性对比，以刻画LSTM的学习动态和记忆收敛。大量仿真表明，X-RACE将推理复杂度降低至少44.1%，同时改善或保持误码率（BER）性能，优于经典XAI方案。
英文摘要
Deep learning models, notably Long Short-Term Memory (LSTM), have demonstrated promising performance in channel estimation for high-mobility vehicular environments. However, their black-box nature and architectural overhead limit trustworthiness and efficiency. Classical explainable AI (XAI) methods rely on costly iterative processes, offering only input-level filtering without addressing architectural fine-tuning. To overcome these limitations, this paper proposes the XAI-assisted Recurrent neural network Attribution for Channel Estimation (X-RACE) framework. X-RACE uses a low-complexity, one-shot dual-optimization strategy to simultaneously evaluate and prune irrelevant input subcarriers and internal hidden units. Furthermore, we propose novel temporal XAI metrics: Saturation Time, Importance Drift, and Relevance Contrast to characterize the LSTM's learning dynamics and memory convergence. Extensive simulations demonstrate that X-RACE reduces inference complexity by at least 44.1% while improving or preserving Bit Error Rate (BER) performance, outperforming classical XAI schemes.
发表机构
- University of Paris-Est Créteil (UPEC)（巴黎东克雷泰伊大学（UPEC））
- IMT Nord Europe（法国北部国立高等电信学院）
机构由 AI 辅助整理，请以论文原文为准。
