X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation
arXiv:2602.22277v1 Announce Type: new Abstract: AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based XAI solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons. This enables a holistic optimization that identifies the most faithful model components. Simulation results demonstrate that X-REFINE achieves a superior interpretability-performance-complexity trade-off, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance across
arXiv:2602.22277v1 Announce Type: new Abstract: AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based XAI solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons. This enables a holistic optimization that identifies the most faithful model components. Simulation results demonstrate that X-REFINE achieves a superior interpretability-performance-complexity trade-off, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance across different scenarios.
Executive Summary
This article proposes X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning in AI-native architectures for 6G wireless communications. X-REFINE utilizes a decomposition-based, sign-stabilized LRP epsilon rule to derive high-resolution relevance scores for subcarriers and hidden neurons, enabling holistic optimization and superior interpretability-performance-complexity trade-off. Simulation results demonstrate reduced computational complexity while maintaining robust BER performance. The framework's ability to fine-tune architecture and filter inputs could lead to more efficient and reliable channel estimation in 6G wireless communications.
Key Points
- ▸ X-REFINE proposes an XAI-based framework for joint input-filtering and architecture fine-tuning
- ▸ Decomposition-based, sign-stabilized LRP epsilon rule is used to derive high-resolution relevance scores
- ▸ Simulation results demonstrate superior interpretability-performance-complexity trade-off
Merits
Robustness to Different Scenarios
X-REFINE maintains robust BER performance across various scenarios, ensuring reliable channel estimation
Efficient Computational Complexity
X-REFINE significantly reduces computational complexity while maintaining performance
Demerits
Limited Generality
The framework's effectiveness may be limited to the specific 6G wireless communication context
Complexity of LRP Epsilon Rule
The decomposition-based, sign-stabilized LRP epsilon rule may introduce additional computational complexity
Expert Commentary
X-REFINE represents a significant advancement in AI-native architectures for 6G wireless communications. By leveraging XAI techniques for joint input-filtering and architecture fine-tuning, the framework offers a superior interpretability-performance-complexity trade-off. However, the complexity of the LRP epsilon rule and the limited generality of the framework's effectiveness may pose challenges for widespread adoption. Nevertheless, X-REFINE has the potential to revolutionize channel estimation in 6G wireless communications, making it a crucial development for the field.
Recommendations
- ✓ Further research should focus on adapting X-REFINE to other AI-native architectures beyond 6G wireless communications
- ✓ The development of more efficient and scalable LRP epsilon rule variants could enhance the framework's practical applicability
