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

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.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI)

arXiv:2609.11211 [eess.SP]

(or arXiv:2609.11211v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.11211 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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