Research into 6G networks has been initiated to support a variety of critical artificial intelligence (AI) assisted applications such as autonomous driving. In such applications, AI-based decisions should be performed in a real-time manner. These decisions include resource allocation, localization, channel estimation, etc. Considering the black-box nature of existing AI-based models, it is highly challenging to understand and trust the decision-making behavior of such models. Therefore, explaining the logic behind those models through explainable AI (XAI) techniques is essential for their employment in critical applications. This manuscript proposes a novel XAI-based channel estimation (XAI-CHEST) scheme that provides detailed reasonable interpretability of the deep learning (DL) models that are employed in doubly-selective channel estimation. The aim of the proposed XAI-CHEST scheme is to identify the relevant model inputs by inducing high noise on the irrelevant ones. As a result, the behavior of the studied DL-based channel estimators can be further analyzed and evaluated based on the generated interpretations. Simulation results show that the proposed XAI-CHEST scheme provides valid interpretations of the DL-based channel estimators for different scenarios.

翻译：针对6G网络的研究已启动，以支持自主驾驶等多种关键人工智能（AI）辅助应用。在此类应用中，基于AI的决策需实时执行，这些决策涉及资源分配、定位、信道估计等。鉴于现有AI模型的"黑箱"特性，理解并信任这些模型的决策行为极具挑战性。因此，通过可解释人工智能（XAI）技术阐明模型背后的逻辑，对于其在关键应用中的部署至关重要。本文提出一种新型基于XAI的信道估计（XAI-CHEST）方案，该方案可为用于双选择性信道估计的深度学习（DL）模型提供详细合理的可解释性。所提出的XAI-CHEST方案旨在通过向无关输入施加高噪声来识别相关模型输入。基于生成的解释，可进一步分析并评估所研究的DL信道估计器的行为。仿真结果表明，所提出的XAI-CHEST方案能为不同场景下基于DL的信道估计器提供有效的解释。