# Knowledge-powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G

arXiv: https://arxiv.org/abs/2205.05406 · HTML: https://mlsanity.com/paper/2205.05406/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2022.

## Built on by (most cited first, 4 of 4)

- [Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case](https://mlsanity.com/paper/2307.12903/) (2023)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing](https://mlsanity.com/paper/2210.10147/) (2022)
- [Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions](https://mlsanity.com/paper/2606.10942/) (2026)

## Abstract (arXiv)

Communication networks are becoming increasingly complex towards 6G. Manual management is no longer an option for network operators. Network automation has been widely discussed in the networking community, and it is a sensible means to manage the complex communication network. Deep learning models developed to enable network automation for given operation practices have the limitations of 1) lack of explainability and 2) inapplicable across different networks and/or network settings. To tackle the above issues, in this article we propose a new knowledge-powered framework that provides a human-understandable explainable artificial intelligence (XAI) agent for network automation. A case study of path selection is developed to demonstrate the feasibility of the proposed framework. Research on network automation is still in its infancy. Therefore, at the end of this article, we provide a list of challenges and open issues that can guide further research in this important area.