Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
This work addresses the need for AI-driven analytics in 5G/6G networks to support high data rates and low latencies for connected devices, though it is incremental as it builds on existing NWDAF frameworks.
The paper implemented a functional Network Data Analytics Function (NWDAF) in a 5G core network using open-source software to analyze network data and provide insights for intelligent management, such as detecting patterns to improve performance.
Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices. Requirements such as high data rates, low latencies, and reliability are crucial considerations and artificial intelligence is incorporated to achieve these requirements for a large number of connected devices. Specifically, intelligent methods and frameworks for advanced analysis are employed by the 5G Core Network Data Analytics Function (NWDAF) to detect patterns and ascribe detailed action information to accommodate end users and improve network performance. To this end, the work presented in this paper incorporates a functional NWDAF into a 5G network developed using open source software. Furthermore, an analysis of the network data collected by the NWDAF and the valuable insights which can be drawn from it have been presented with detailed Network Function interactions. An example application of such insights used for intelligent network management is outlined. Finally, the expected limitations of 5G networks are discussed as motivation for the development of 6G networks.