Network Meets ChatGPT: Intent Autonomous Management, Control and Operation

Abstract

Telecommunication has undergone significant transformations due to the continuous advancements in internet technology, mobile devices, competitive pricing, and changing customer preferences. Specifically, the most recent iteration of OpenAI's large language model chat generative pre-trained transformer (ChatGPT) has the potential to propel innovation and bolster operational performance in the telecommunications sector. Nowadays, the exploration of network resource management, control, and operation is still in the initial stage. In this paper, we propose a novel network artificial intelligence architecture named language model for network traffic (NetLM), a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics. The continual convergence of knowledge space and artificial intelligence (AI) technologies constitutes the core of intelligent network management and control. Multi-modal representation learning is used to unify the multi-modal information of network indicator data, traffic data, and text data into the same feature space. Furthermore, a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels. Finally, some potential cases are provided that NetLM can benefit the telecom industry.