Signal Theory and Communications Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain.
Sensors (Basel). 2023 Jan 20;23(3):1197. doi: 10.3390/s23031197.
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%.
网络数字孪生 (NDT) 是真实网络的高保真数字镜像。考虑到 5G 及以后网络的日益复杂性，在将配置和算法应用于实际网络之前，使用 NDT 作为平台进行测试变得很有用，同时也可以预测这些算法在不同条件下的性能。虽然可以为网络的不同子系统定义 NDT，但本文提出了一种专注于无线接入网 (RAN) 的 NDT 架构，描述了用于表示和模拟不同 RAN 元素的操作并进行仿真的组件，以及不同的应用用例进行了识别，并重点介绍了针对 RAN 的强化学习 (RL) 解决方案的培训。对于这种用例，本文引入了一个与 O-RAN 规范保持一致的框架，并讨论了集成 NDT 所需的功能。通过描述用于培训基于 RL 的网络切片容量共享解决方案的 RAN NDT 实现，说明了该用例。所呈现的结果表明，所实现的 RAN NDT 是成功训练 RL 解决方案的合适平台，实现了超过 85%的服务级别协议满意度值。
