A Real-time Data Collection Approach for 6G AI-native Networks
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911132194701312 |
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| author | Shiwen, He Haolei, Dong Liangpeng, Wang Zhenyu, An |
| author_facet | Shiwen, He Haolei, Dong Liangpeng, Wang Zhenyu, An |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-time Data Collection Approach for 6G AI-native Networks Shiwen, He Haolei, Dong Liangpeng, Wang Zhenyu, An Networking and Internet Architecture 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. |
| title | A Real-time Data Collection Approach for 6G AI-native Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.01276 |