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

arXiv: https://arxiv.org/abs/2401.03310 · HTML: https://mlsanity.com/paper/2401.03310/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 78 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation](https://mlsanity.com/paper/2303.17063/) (2023)
- [Networked Twins and Twins of Networks: an Overview on the Relationship Between Digital Twins and 6G](https://mlsanity.com/paper/2108.05781/) (2021)
- [Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse](https://mlsanity.com/paper/2210.04606/) (2022)
- [A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments](https://mlsanity.com/paper/2303.06947/) (2023)
- [Generating MIMO Channels For 6G Virtual Worlds Using Ray-tracing Simulations](https://mlsanity.com/paper/2106.05377/) (2021)
- [Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection](https://mlsanity.com/paper/2301.08973/) (2023)
- [Simulating Hybrid Aerial- and Ground-based Vehicular Networks with ns-3 and LIMoSim](https://mlsanity.com/paper/2003.09829/) (2020)
- [Twinning Commercial Radio Waveforms in the Colosseum Wireless Network Emulator](https://mlsanity.com/paper/2308.04123/) (2023)
- [Hybrid Precoding and Combining for mmWave Full-Duplex Joint Radar and Communication Systems under Self-Interference](https://mlsanity.com/paper/2311.14942/) (2023)

## Built on by (most cited first, 3 of 3)

- [Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G](https://mlsanity.com/paper/2507.08716/) (2025)
- [Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study](https://mlsanity.com/paper/2609.18704/) (2026)
- [Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning](https://mlsanity.com/paper/2610.02370/) (2026)

## Abstract (arXiv)

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.