论文标题
知识驱动的可解释的人工智能（XAI），用于6G的网络自动化
Knowledge-powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G
论文作者
论文摘要
通信网络对6G的越来越复杂。对于网络运营商而言，手动管理不再是一种选择。网络自动化已在网络社区中广泛讨论，这是管理复杂通信网络的明智手段。为给定的操作实践启用网络自动化而开发的深度学习模型的局限性为1）缺乏解释性和2）在不同的网络和/或网络设置之间不适用。为了解决上述问题，在本文中，我们提出了一个新的知识驱动框架，该框架为网络自动化提供了可解释的人工智能（XAI）代理。开发了一项路径选择的案例研究，以证明所提出的框架的可行性。网络自动化的研究仍处于起步阶段。因此，在本文结尾处，我们提供了一系列挑战和开放问题，可以指导这一重要领域的进一步研究。
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.
