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

TL;DR

This paper proposes a knowledge-powered XAI framework for network automation in 6G, addressing the limitations of deep learning models by enhancing explainability and adaptability across different network settings.

Contribution

It introduces a novel framework that combines knowledge-based approaches with XAI for more transparent and adaptable network automation solutions.

Findings

Demonstrated feasibility through a path selection case study

Identified key challenges and open issues in network automation research

Highlighted the importance of explainability for future 6G networks

Abstract

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.