X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation
arXiv:2602.22277v2 Announce Type: replace 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 eXplainable Artificial Intelligence (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 rul
The increasing complexity of AI models in critical infrastructure like 6G communication necessitates advanced interpretability and optimization techniques to overcome deployment hurdles.
This development addresses key limitations of AI in critical applications by improving transparency and efficiency, which is crucial for the secure and reliable adoption of AI-native systems.
The ability to fine-tune AI architectures and filter input based on XAI significantly reduces the 'black-box' problem, making AI more trustworthy and applicable in sensitive environments.
- · Telecommunications infrastructure providers
- · AI model developers
- · Cybersecurity firms
- · 6G technology early adopters
- · Developers of proprietary black-box AI models
- · Traditional communication system designers
- · Industries reliant on opaque AI deployments
Improved reliability and explainability of AI in 6G channel estimation.
Faster and wider adoption of AI-native architectures across other critical infrastructure sectors.
Enhanced national security through more resilient and understandable AI-driven communication networks, contributing to sovereign AI capabilities.
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Read at arXiv cs.LG