分散虚拟开放无线接入网（O-RAN）中的多Agent团队学习
Multi Agent Team Learning in Disaggregated Virtualized Open Radio Access Networks (O-RAN)
摘要：
从云无线电接入网络（C-RAN）开始，继续虚拟无线电接入网络（vRAN），以及最近的开放无线电接入网络（O-RAN）倡议，无线电接入网络（RAN）架构在过去十年中发生了重大变化。在过去几年中，无线行业见证了分散、虚拟化和开放RAN的强劲趋势，并在全球范围内进行了大量测试和部署。激发本文的一个独特方面是使用机器学习来优化闭环中的RAN（即，没有人为干预）所产生的新机会的可用性，其中解聚和虚拟化的复杂性使得众所周知的自组织网络（SON）解决方案不足。在我们看来，多智能体系统（MAS）与团队学习，可以发挥重要作用的控制和协调控制器的O-RAN，即近实时和非实时RAN智能控制器（RIC）。在这篇文章中，我们首先介绍了多智能体系统和团队学习的最新研究，然后我们概述了RAN解聚和虚拟化的前景，以及O-RAN，它强调了O-RAN联盟引入的开放接口。我们提出了一个案例研究代理的位置和O-RAN中所需的AI反馈，最后，我们确定了挑战和开放的问题，为研究人员提供了一个路线图。
英文摘要：
Starting from the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments world wide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning to optimize the RAN in closed-loop, i.e. without human intervention, where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with team learning, can play an essential role in the control and coordination of controllers of O-RAN, i.e. near-real-time and non-real-time RAN Intelligent Controller (RIC). In this article, we first present the state-of-the-art research in multi-agent systems and team learning, then we provide an overview of the landscape in RAN disaggregation and virtualization, as well as O-RAN which emphasizes the open interfaces introduced by the O-RAN Alliance. We present a case study for agent placement and the AI feedback required in O-RAN, and finally, we identify challenges and open issues to provide a roadmap for researchers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
