# Towards Explainable AI for Channel Estimation in Wireless Communications

arXiv: https://arxiv.org/abs/2307.00952 · HTML: https://mlsanity.com/paper/2307.00952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [A General Framework for Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/1907.06890/) (2019)
- [Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks](https://mlsanity.com/paper/2107.11400/) (2021)
- [U-Noise: Learnable Noise Masks for Interpretable Image Segmentation](https://mlsanity.com/paper/2101.05791/) (2021)
- [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022)
- [A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments](https://mlsanity.com/paper/2206.02165/) (2022)
- [CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications](https://mlsanity.com/paper/2104.08813/) (2021)

## Built on by (most cited first, 5 of 5)

- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification](https://mlsanity.com/paper/2401.13206/) (2024)
- [BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks](https://mlsanity.com/paper/2602.14033/) (2026)
- [REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation](https://mlsanity.com/paper/2606.11857/) (2026)

## Abstract (arXiv)

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.