Knowledge-powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G
Wu, Y; Lin, G; Ge, J
Date: 13 July 2022
Article
Journal
IEEE Network
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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