Towards Explainable AI for Channel Estimation in Wireless Communications

TL;DR

This paper introduces XAI-CHEST, a novel explainable AI scheme for channel estimation in wireless communications, enhancing interpretability of deep learning models used in 6G networks for critical applications.

Contribution

It proposes a new XAI method that identifies relevant inputs in deep learning-based channel estimators, improving understanding and trust in AI decisions for wireless systems.

Findings

Provides valid interpretations across different scenarios

Enhances trust in AI-based channel estimation models

Improves understanding of deep learning behavior in wireless channels

Abstract

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.