The support of artificial intelligence (AI) based decision-making is a key element in future 6G networks, where the concept of native AI will be introduced. Moreover, AI is widely employed in different critical applications such as autonomous driving and medical diagnosis. In such applications, using AI as black-box models is risky and challenging. Hence, it is crucial to understand and trust the decisions taken by these models. Tackling this issue can be achieved by developing explainable AI (XAI) schemes that aim to explain the logic behind the black-box model behavior, and thus, ensure its efficient and safe deployment. Recently, we proposed a novel perturbation-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications. The core idea of the XAI-CHEST framework is to identify the relevant model inputs by inducing high noise on the irrelevant ones. This manuscript provides the detailed theoretical foundations of the XAI-CHEST framework. In particular, we derive the analytical expressions of the XAI-CHEST loss functions and the noise threshold fine-tuning optimization problem. Hence the designed XAI-CHEST delivers a smart input feature selection methodology that can further improve the overall performance while optimizing the architecture of the employed model. Simulation results show that the XAI-CHEST framework provides valid interpretations, where it offers an improved bit error rate performance while reducing the required computational complexity in comparison to the classical DL-based channel estimation.

翻译：基于人工智能的决策支持是未来6G网络的关键要素，届时将引入原生AI的概念。此外，AI已广泛应用于自动驾驶、医疗诊断等不同关键领域。在此类应用中，将AI作为黑盒模型使用存在风险与挑战。因此，理解并信任这些模型所作决策至关重要。解决这一问题可通过开发可解释人工智能方案来实现，该方案旨在阐释黑盒模型行为背后的逻辑，从而确保其高效安全的部署。近期，我们提出了一种面向无线通信信道估计的新型基于扰动的XAI-CHEST框架。该框架的核心思想是通过对不相关输入施加高强度噪声来识别相关模型输入。本文详细阐述了XAI-CHEST框架的理论基础，具体推导了XAI-CHEST损失函数的解析表达式及噪声阈值微调优化问题。由此设计的XAI-CHEST提供了一种智能输入特征选择方法，可在优化所采用模型架构的同时进一步提升整体性能。仿真结果表明，相较于传统基于深度学习的信道估计方法，XAI-CHEST框架能提供有效解释，在降低计算复杂度的同时实现了更优的误码率性能。