Digital twins are an important technology for advancing mobile communications, specially in use cases that require simultaneously simulating the wireless channel, 3D scenes and machine learning. Aiming at providing a solution to this demand, this work describes a modular co-simulation methodology called CAVIAR. Here, CAVIAR is upgraded to support a message passing library and enable the virtual counterpart of a digital twin system using different 6G-related simulators. The main contributions of this work are the detailed description of different CAVIAR architectures, the implementation of this methodology to assess a 6G use case of UAV-based search and rescue mission (SAR), and the generation of benchmarking data about the computational resource usage. For executing the SAR co-simulation we adopt five open-source solutions: the physical and link level network simulator Sionna, the simulator for autonomous vehicles AirSim, scikit-learn for training a decision tree for MIMO beam selection, Yolov8 for the detection of rescue targets and NATS for message passing. Results for the implemented SAR use case suggest that the methodology can run in a single machine, with the main demanded resources being the CPU processing and the GPU memory.

翻译：数字孪生是推动移动通信发展的重要技术，尤其是在需要同时仿真无线信道、三维场景和机器学习等场景中。为满足这一需求，本文提出了一种名为CAVIAR的模块化协同仿真方法。本文对CAVIAR进行升级，以支持消息传递库，并利用多个与6G相关的仿真器实现数字孪生系统的虚拟对应体。本文的主要贡献包括：详细阐述不同CAVIAR架构、将该方法应用于评估基于无人机的搜索与救援（SAR）任务这一6G用例，以及生成计算资源使用情况的基准数据。在执行SAR协同仿真时，我们采用了五种开源方案：物理层与链路级网络仿真器Sionna、自主车辆仿真器AirSim、用于训练MIMO波束选择决策树的scikit-learn、用于检测救援目标的Yolov8，以及用于消息传递的NATS。针对所实现的SAR用例，结果表明该方法可在单台机器上运行，主要消耗的资源为CPU处理能力和GPU内存。