Towards Explainable AI for Channel Estimation in Wireless Communications

• URL: http://arxiv.org/abs/2307.00952v2
• Date: Wed, 6 Dec 2023 08:20:22 GMT
• Title: Towards Explainable AI for Channel Estimation in Wireless Communications
• Authors: Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier
• Abstract summary: 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.
• Score: 1.0874597293913013
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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