Title: A Novel Integrated Architecture for Intent Based Approach and Zero Touch Networks Show Chinese title

Title: 一种基于意图的方法和零接触网络的新型集成架构

Abstract: The transition to Sixth Generation (6G) networks presents challenges in managing quality of service (QoS) of diverse applications and achieving Service Level Agreements (SLAs) under varying network conditions. Hence, network management must be automated with the help of Machine Learning (ML) and Artificial Intelligence (AI) to achieve real-time requirements. Zero touch network (ZTN) is one of the frameworks to automate network management with mechanisms such as closed loop control to ensure that the goals are met perpetually. Intent- Based Networking (IBN) specifies the user intents with diverse network requirements or goals which are then translated into specific network configurations and actions. This paper presents a novel architecture for integrating IBN and ZTN to serve the intent goals. Users provides the intent in the form of natural language, e.g., English, which is then translated using natural language processing (NLP) techniques (e.g., retrieval augmented generation (RAG)) into Network Intent LanguagE (Nile). The Nile intent is then passed on to the BiLSTM and Q-learning based ZTN closed loop framework as a goal which maintains the intent under varying network conditions. Thus, the proposed architecture can work autonomously to ensure the network performance goal is met by just specifying the user intent in English. The integrated architecture is also implemented on a testbed using OpenAirInterface (OAI). Additionally, to evaluate the architecture, an optimization problem is formulated which evaluated with Monte Carlo simulations. Results demonstrate how ZTN can help achieve the bandwidth goals autonomously set by user intent. The simulation and the testbed results are compared and they show similar trend. Mean Opinion Score (MOS) for Quality of Experience (QoE) is also measured to indicate the user satisfaction of the intent. Show Chinese abstract

Abstract: 第六代（6G）网络的过渡在管理多样化应用的服务质量（QoS）和在不同网络条件下实现服务等级协议（SLAs）方面带来了挑战。因此，必须借助机器学习（ML）和人工智能（AI）来自动化网络管理，以满足实时需求。零接触网络（ZTN）是一种通过闭环控制等机制自动化网络管理的框架，以确保目标持续达成。基于意图的网络（IBN）用不同的网络需求或目标来指定用户意图，然后将其转换为具体的网络配置和操作。本文提出了一种集成IBN和ZTN的新架构，以实现意图目标。用户以自然语言（例如英语）形式提供意图，然后使用自然语言处理（NLP）技术（例如检索增强生成（RAG））将其翻译成网络意图语言（Nile）。然后将Nile意图作为目标传递给基于BiLSTM和Q-learning的ZTN闭环框架，该框架在不同网络条件下维持意图。因此，所提出的架构可以自主运行，只需指定用户的英语意图即可确保网络性能目标的实现。该集成架构还使用OpenAirInterface（OAI）在测试平台上进行了实现。此外，为了评估该架构，制定了一项优化问题，并通过蒙特卡洛模拟进行评估。结果展示了ZTN如何帮助自主实现由用户意图设定的带宽目标。仿真和测试平台结果进行了比较，显示出相似的趋势。还测量了用户体验质量（QoE）的平均意见分数（MOS），以表明用户对意图的满意度。