CAVIAR：面向数字孪生的6G通信、3D场景与AI联合仿真
CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital Twins
AI总结：
本文提出名为 CAVIAR 的模块化联合仿真方法，支持消息传递库并整合多种6G相关仿真器实现数字孪生系统，通过基于无人机的搜救任务用例验证其可在单机上运行且主要依赖 CPU 和 GPU 内存。
摘要：
数字孪生是推动移动通信发展的重要技术，特别是在需要同时仿真无线信道、3D场景和机器学习的用例中。为满足这一需求，本文描述了一种名为 CAVIAR 的模块化联合仿真方法。在此，CAVIAR 被升级以支持消息传递库，并利用不同的6G相关仿真器实现数字孪生系统的虚拟对应物。本文的主要贡献包括：详细描述了不同的 CAVIAR 架构，将该方法的实现用于评估基于无人机的搜救任务（SAR）的6G用例，以及生成有关计算资源使用的基准数据。为执行 SAR 联合仿真，我们采用了五种开源解决方案：物理和链路级网络仿真器 Sionna、自动驾驶仿真器 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。已实现的 SAR 用例结果表明，该方法可在单机上运行，主要需求的资源是 CPU 处理能力和 GPU 内存。
英文摘要：
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.
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。
