  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.
