论文标题
知识转移和再利用：在切片中对支持AI的资源管理的案例研究
Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing
论文作者
论文摘要
有效的资源管理方案对于在5G网络和设想的6G网络中启用网络切片至关重要，人工智能（AI）技术提供了有希望的解决方案。考虑到迅速新兴的新机器学习技术，例如图形学习，联合学习和转移学习，需要及时进行调查，以提供对AI启用的无线网络的资源管理和网络切片技术的概述。本文提供了这样的调查以及无线电访问网络（RAN）切片中知识转移的应用。特别是，我们FIR提供了一些有关资源管理和网络切片的背景，并查看相关的最新AI和机器学习（ML）技术及其应用。然后，我们介绍了基于AI的知识转移和基于重用的资源管理（AKRM）方案，在其中我们应用转移学习来提高系统性能。与大多数现有的作品相比，重点是从头开始训练独立代理的培训，AKRM的主要区别在于其知识转移和不同任务之间的重用能力。我们的论文旨在使研究人员使用AI-abable无线网络中的知识转移方案的路线图，我们提供了有关STRICING中资源分配问题的案例研究。
An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we firs provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.
