Abstract:

Autonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The deployment phase of autonomous networks was systematically studied.Firstly, it elaborated on the independent deployment mode and full stack deployment mode of autonomous networks, and pointed out that full stack deployment was the main direction.Secondly, a detailed introduction was given to the full stack architecture with “five layers, dual domains, and four closed-loops”, which achieved full life cycle intelligence through a layered closed-loop design of resources and processes.Then, three core technologies for independent innovation were proposed: AI model training and inference integration to achieve rapid iterative updates of models, AI fabric technology to achieve customized application by rapid construction, and AI model cloud-edge collaborative deployment technology to achieve efficient application.Finally, the effectiveness of these three core technologies was verified through cases such as anomaly detection, smart telecommunication rooms, and equipment inspections.The deployment of autonomous networks was systematically explored, especially in terms of architecture design and core technology innovation, which had important reference value for telecommunication operators’ network digital transformation.

Key words: autonomous network, full stack deployment, training and inference integration, cloud edge collaborative deployment, AI fabric