To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.

翻译：为有效表达并满足网络应用需求，基于意图的网络管理已成为一种富有前景的解决方案。在基于意图的方法中，用户和应用以高层抽象语言向网络表达其意图。尽管这种抽象简化了网络操作，但给高效表达应用意图并将其映射到不同网络能力带来了诸多挑战。因此，本文提出了一种基于人工智能的意图画像与翻译框架。我们考虑了与应用交互的网络在其领域语言中表达网络服务需求的场景。机器间通信（即应用与网络之间）因其要求网络学习理解各应用的领域语言而变得复杂，这种做法既不切实也不具备可扩展性。为此，本文提出了一种基于涌现通信的意图画像框架，其中应用通过涌现通信消息向网络表达其抽象体验质量意图。随后，网络学习如何解读这些通信消息并将其映射到网络能力（即切片），以保障所请求的服务质量。仿真结果表明，所提方法优于自学习切片及其他基线方法，其性能接近完美知识基线。