X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation
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 subcarrier...
| Authors | Abdul Karim Gizzini, Yahia Medjahdi |
|---|---|
| Primary category | eess.SP |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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