意图驱动的自智网络资源按需服务

6G自智网络需要实现面向多层用户的网络自动化全场景按需服务，运营商用户亟需一种有效挖掘多层用户意图并实现资源自动化按需分配的方法，为此，提出了一种将用户意图转为策略对网络资源进行管理的全自动化框架。首先，考虑到意图挖掘数据的稀缺性，提出一种利用无标注语料以提高意图实体挖掘能力的方法。其次，综合考虑网络服务质量和用户业务需求，利用深度强化学习算法，对网络资源的划分进行优化和管理，提升用户使用体验的同时使网络负载均衡，资源达到最大化利用。实验结果表明，所提框架能够更准确挖掘用户意图、更精确划分网络资源，从而保障服务质量。

Intent-driven Demand-aware Resource Service in Autonomous Networks

6G Autonomous Networks needs to realize the network automation full scene demand-aware service for multi-layer users. Operators' users urgently need a method to effectively mine the multi-layer user intents and realize the automatic on-demand allocation of resources. Therefore, a fully automated framework for managing network resources by turning user intention into strategy is proposed. Firstly, considering the scarcity of intent mining data, a method of using unlabeled corpus to improve the ability of intent entity mining is proposed. Secondly, comprehensively considering the network service quality and user business needs, the deep reinforcement learning algorithm is used to optimize and manage the division of network resources, improve the user experience, balance the network load and maximize the utilization of resources. Experimental results show that the proposed framework can mine users' intents more rapidly and divide network resources more accurately to ensure the quality of service.