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Towards Explainable AI for Channel Estimation in Wireless Communications
Artificial Intelligence · Submitted 3 Jul 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Research into 6G networks has been initiated to support a variety of critical artificial intelligence (AI) assisted applications such as autonomous driving. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | In such applications, AI-based decisions should be performed in a real-time manner. | background 1.00 | 0.04 | 0.02 | asserted 0.99 |
| s2 | These decisions include resource allocation, localization, channel estimation, etc. | background 1.00 | 0.04 | 0.02 | asserted 0.96 |
| s3 | 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. | background 0.73 | 0.05 | 0.02 | asserted 0.99 |
| s4 | Therefore, explaining the logic behind those models through explainable AI (XAI) techniques is essential for their employment in critical applications. | background 0.85 | 0.04 | 0.06 | asserted 1.00 |
| s5 | 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. | contribution 0.99 | 0.90 | 0.22 | asserted 1.00 |
| s6 | The aim of the proposed XAI-CHEST scheme is to identify the relevant model inputs by inducing high noise on the irrelevant ones. | method 0.96 | 0.67 | 0.07 | asserted 1.00 |
| s7 | As a result, the behavior of the studied DL-based channel estimators can be further analyzed and evaluated based on the generated interpretations. | interpretation 0.39 | 0.52 | 0.12 | asserted 0.70 |
| s8 | Simulation results show that the proposed XAI-CHEST scheme provides valid interpretations of the DL-based channel estimators for different scenarios. | result 1.00 | 0.95 | 0.79 | asserted 1.00 |
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The paper
Original 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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