Summary of Explainable Ai For Enhancing Efficiency Of Dl-based Channel Estimation, by Abdul Karim Gizzini et al.
Explainable AI for Enhancing Efficiency of DL-based Channel Estimation
by Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed XAI-CHEST framework is a novel perturbation-based explainable AI (XAI) scheme designed for channel estimation in wireless communications. It aims to identify relevant model inputs by inducing high noise on irrelevant ones, ensuring efficient and safe deployment of black-box models. The framework provides a smart input feature selection methodology that improves overall performance while optimizing the architecture of employed models. Simulation results show XAI-CHEST delivers valid interpretations, offering improved bit error rate performance with reduced computational complexity compared to classical DL-based channel estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop an AI system that can explain its decisions. This is important because AI systems are used in critical applications like self-driving cars and medical diagnosis. The proposed XAI-CHEST framework helps identify which inputs a model relies on by adding noise to unimportant ones. This makes the system safer and more efficient. The paper provides the mathematical foundation for this approach, showing how it can improve performance while reducing complexity. |
Keywords
» Artificial intelligence » Feature selection
