意图画像与翻译：通过涌现通信实现
Intent Profiling and Translation Through Emergent Communication
AI总结：
本文提出基于涌现通信的AI框架，用于网络应用意图画像与翻译，通过将应用QoE意图映射到网络切片，实现高效意图表达，仿真验证其性能优于自学习切片并接近完美知识基线。
摘要：
为了有效表达和满足网络应用需求，基于意图的网络管理已成为一种有前景的解决方案。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，但它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，在本工作中，我们提出了一种基于AI的意图画像与翻译框架。我们考虑一个场景：与网络交互的应用以其领域语言表达对网络服务的需求。机器对机器通信（即应用与网络之间）是复杂的，因为它要求网络学习理解每个应用的领域语言，这既不实际也不可扩展。相反，我们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（QoE）意图。随后，网络学习如何解释这些通信消息并将其映射到网络能力（即切片），以保证所请求的服务质量（QoS）。仿真结果表明，所提出的方法优于自学习切片和其他基线方法，并达到了接近完美知识基线的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
