NIDA Self-Intelligent Network Workgroup Releases the First L4 Network Construction Standard for the Education Industry
[Shanghai, China, July 27, 2025] During the 2025 World Conference on Artificial Intelligence (WAIC), The Global Fixed Network Innovation Alliance (NIDA) has launched a collaboration between the Self-Intelligent Network Working Group and 12 universities, including China Research Institute of Information and Communication, Huawei, Shandong University, China University of Mining and Technology, Shanghai University of Science and Technology, Wenzhou Medical University, and Jiangsu Institute for Future Network Innovation. The Beijing Research Institute of China Telecom Co., Ltd. and the Research Institute of China United Network Communications Co., Ltd. jointly developed the world's first self-intelligent network construction standard for the education industry in the enterprise domain. The Technical Requirements for Agent-Based Campus Network L4 O&M (hereinafter referred to as "Standard") provides a set of forward-looking and feasible technical guidance for the construction and testing of the campus network L4 O&M system.
With the rapid increase in the number of network devices and terminals on campus, the service requirements are becoming more and more complex, and teachers and students have higher and higher requirements on network service quality. However, in an existing O&M mode, managing and troubleshooting thousands of devices is time-consuming and labor-intensive, which severely affects user experience and increases the customer complaint rate. Fortunately, with the in-depth integration of AI and network technologies, we have a new opportunity to carry out research on closed-loop management of enterprise self-intelligent networks and promote intelligent enterprise network O&M systems.
Based on the high-value scenario planning for L4 evolution of the self-intelligent network in the carrier domain and the practice experience of campus network O&M, the standard defines the L1 to L5 levels of the self-intelligent network O&M capability to improve O&M efficiency and dynamically optimize network performance to ensure user experience. Based on smart campus O M requirements and campus network O M experience, Wi-Fi network optimization and monitoring and troubleshooting are selected as high-value scenarios for campus network O M, and the target architecture for smart network implementation is defined. Define technical requirements based on intent understanding, environment awareness, data analysis, policy decision-making, and solution execution of the O&M management system to build a network AI brain model.
The Technical Requirements for Agent-Based Campus Network L4 O&M points out the evolution direction and implementation path for campus network intelligence. The key technologies are network digital twin, large network model, and small algorithm model, to build a task-level self-intelligence closed-loop system for campus network agents. It will accelerate the intelligent network O&M of colleges and universities across the country to the new era of L4 self-intelligent networks.
