The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.

翻译：近期O-RAN规范通过功能解耦、开放接口的采用以及由RAN智能控制器（RIC）实体管理的分层闭环控制架构实例化，推动了RAN架构的演进。这为基于可编程逻辑的新型数据驱动网络管理方法铺平了道路。借助人工智能（AI）和机器学习（ML），能够设计出针对传统未解决的RAN管理问题的新颖解决方案。然而，此类智能自主系统的采用受限于当前人类操作员无法理解AI/ML解决方案的决策过程，从而影响了他们对这些新型工具的信任。可解释人工智能（XAI）旨在解决这一问题，使人类用户能够更好地理解并有效管理新兴的智能方案体系，降低人机交互障碍。本综述首先总结了XAI方法与评估指标，随后研究了其在O-RAN联盟RAN架构及其主要构建模块上的部署。接着，我们介绍了多种用例，探讨了O-RAN中XAI管道的自动化及其相关安全方面，并回顾了涉及该领域的部分项目与标准。最后，我们识别了在此背景下大量采用AI/ML决策实体可能引发的各种挑战与研究方向，重点关注XAI如何帮助解释、理解并增强对O-RAN运营网络的信任。