Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation

The support of artificial intelligence (AI) based decision-making is a key element in future 6G networks. Moreover, AI is widely employed in 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. Highlighting the relevant inputs the black-box model uses to accomplish the desired prediction is essential towards ensuring its interpretability. Recently, we proposed a novel perturbation-based feature selection framework called XAI-CHEST and oriented toward channel estimation in wireless communications. 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 low-complex one-shot input feature selection methodology for high-dimensional model input that can further improve the overall performance while optimizing the architecture of the employed model. Simulation results show that the XAI-CHEST framework outperforms the classical feature selection XAI schemes such as local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), mainly in terms of interpretability resolution as well as providing better performance-complexity trade-off.