English(EN) X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation

新的XAI框架提升LSTM在信道估计中的效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-12 04:00

研究人员开发了一个名为X-RACE的新框架，以提高深度学习模型（特别是用于高移动性车辆环境中信道估计的长短期记忆（LSTM）网络）的可解释性和效率。该框架采用了一种新颖的双重优化策略，同时修剪不相关的输入子载波和内部隐藏单元，从而降低了计算复杂度。此外，X-RACE引入了新的时间XAI指标——饱和时间、重要性漂移和相关性对比——来分析LSTM学习动态。模拟结果表明，与传统的XAI方法相比，X-RACE可以将推理复杂度降低44%以上，同时保持或提高误比特率性能。 AI

影响提高了信号处理应用中深度学习模型的效率和可信度。

排序理由该集群包含一篇详细介绍新颖技术框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Abdul Karim Gizzini, Yahia Medjahdi · 2026-09-12 04:00

X-RACE: XAI辅助的循环神经网络归因用于信道估计

arXiv:2609.11211v1 Announce Type: cross Abstract: 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 …

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报道来源 [1]

X-RACE: XAI辅助的循环神经网络归因用于信道估计

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