论文标题
O-RAN资源分配的Actor-Critic网络：XAPP设计，部署和分析
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis
论文作者
论文摘要
Open Radio Access网络（O-RAN）引入了一个新兴的RAN架构，该体系结构可以实现开放性，智能和自动控制。 RAN智能控制器（RIC）提供了设计和部署RAN控制器的平台。 XAPP是通过利用机器学习（ML）算法并在近乎实现的时间内采取行动来承担这一责任的应用程序。尽管这种新体系结构提供了机会，但实用人工智能（AI）的网络控制和自动化解决方案的进度仍然很慢。这主要是因为缺乏用于设计，部署和测试基于AI的XAPP在实际O-RAN网络中完全可执行文件的解决方案。在本文中，我们介绍了一个端到端的O-RAN设计和评估程序，并通过使用两种不同的RL方法进行了详细的讨论，以详细讨论开发基于强化的XAPP（RL），并考虑了最新发布的O-Ran架构和界面。
Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. This is mostly because of the lack of an endto-end solution for designing, deploying, and testing AI-based xApps fully executable in real O-RAN network. In this paper we introduce an end-to-end O-RAN design and evaluation procedure and provide a detailed discussion of developing a Reinforcement Learning (RL) based xApp by using two different RL approaches and considering the latest released O-RAN architecture and interfaces.
