X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation

2026-09-10

AI Summary

本文提出X-RACE框架，通过低复杂度的一次性双优化策略解决高移动车辆环境下的信道估计问题，同时评估和修剪无关输入子载波和内部隐藏单元。

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 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.

Problem

• XAI
• Channel Estimation
• LSTM
• Black-box Nature
• Architectural Overhead

Innovation

• XAI-assisted
• one-shot dual-optimization
• temporal XAI metrics
• Saturation Time
• Importance Drift