论文标题
选择，而不是ho积：O-Ran中人工智能的信息对模型匹配
Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN
论文作者
论文摘要
开放式无线电访问网络（O-RAN）是一个新兴范式，通过该范式，来自不同供应商的虚拟化网络基础结构元素通过开放的标准化接口进行通信。其中的关键要素是RAN Intellignent Controller（RIC），一个基于人工智能（AI）的控制器。传统上，网络中可用的所有数据已用于训练RIC上用于使用的单个AI模型。本文介绍，讨论和评估了不同RIC的多个AI模型实例的创建，并利用某些（或所有）位置的信息进行培训。这带来了GNB，用于控制它们的AI模型之间的灵活关系，并对此类模型进行了培训。使用现实世界痕迹的实验表明，如何使用多个AI模型实例从特定位置选择培训数据可以改善ho积策略之后的传统方法的性能。
Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.
