CAVIAR: Co-Simulation of 6G Communications, 3-D Scenarios, and AI for Digital Twins

Digital twins are an important technology for advancing mobile communications, specially in use cases that require simultaneously simulating the wireless channel, 3-D scenes and machine learning (ML). Aiming at contributing towards a solution to this demand, this work describes a modular co-simulation methodology called CAVIAR, for implementing the virtual counterpart of a digital twin (DT) system. Here, CAVIAR is upgraded to support a message passing library and facilitate 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 unmanned aerial vehicle (UAV)-based search and rescue (SAR), and the generation of benchmarking data about the computational resource usage. For executing the SAR co-simulation we adopt five open-source solutions: 1) the physical and link level network simulator Sionna; 2) the simulator for autonomous vehicles AirSim; 3) scikit-learn for training a decision tree for multiple input–multiple output (MIMO) beam selection; 4) Yolov8 for the detection of rescue targets; and 5) neural autonomic transport system (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.