标题： 一种面向6G AI原生网络的实时数据收集方法 显示英文标题

标题： A Real-time Data Collection Approach for 6G AI-native Networks

摘要： 在第六代（6G）网络的发展过程中，将人工智能（AI）集成到网络系统中已成为焦点，从而产生了AI原生网络的概念。高质量的数据对于开发此类网络至关重要。尽管一些研究已经探索了6G网络中的数据收集和分析，但仍存在重大挑战，特别是在实时数据获取和处理方面。本文提出了一种全面的数据收集方法，该方法与无线通信网络的比特流处理并行运行。通过部署数据探测器，系统在软件定义的无线通信网络中捕获实时网络和系统状态数据。此外，实现了数据支持系统，以集成异构数据，并为AI模型训练和决策提供自动支持。最后，使用OpenAirInterface5G和Open5GS构建了一个6G通信测试平台，并在Kubernetes上进行，同时通过网络流量预测案例研究展示了系统的功能。 显示英文摘要

摘要： During the development of the Sixth Generation (6G) networks, the integration of Artificial Intelligence (AI) into network systems has become a focal point, leading to the concept of AI-native networks. High quality data is essential for developing such networks. Although some studies have explored data collection and analysis in 6G networks, significant challenges remain, particularly in real-time data acquisition and processing. This paper proposes a comprehensive data collection method that operates in parallel with bitstream processing for wireless communication networks. By deploying data probes, the system captures real-time network and system status data in software-defined wireless communication networks. Furthermore, a data support system is implemented to integrate heterogeneous data and provide automatic support for AI model training and decision making. Finally, a 6G communication testbed using OpenAirInterface5G and Open5GS is built on Kubernetes, as well as the system's functionality is demonstrated via a network traffic prediction case study.