# Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G

arXiv: https://arxiv.org/abs/2507.08716 · HTML: https://mlsanity.com/paper/2507.08716/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications](https://mlsanity.com/paper/1911.06257/) (2019)
- [SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)](https://mlsanity.com/paper/2501.07459/) (2025)
- [BostonTwin: the Boston Digital Twin for Ray-Tracing in 6G Networks](https://mlsanity.com/paper/2403.12289/) (2024)
- [LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction](https://mlsanity.com/paper/2410.21351/) (2024)
- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration](https://mlsanity.com/paper/2412.04734/) (2024)
- [CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital Twins](https://mlsanity.com/paper/2401.03310/) (2024)
- [Channel Knowledge Map (CKM)-Assisted Multi-UAV Wireless Network: CKM Construction and UAV Placement](https://mlsanity.com/paper/2207.01931/) (2022)
- [A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization](https://mlsanity.com/paper/2406.03098/) (2024)
- [Near and Far Field Model Mismatch: Implications on 6G Communications, Localization, and Sensing](https://mlsanity.com/paper/2310.06604/) (2023)

## Built on by (most cited first, 2 of 2)

- [Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network](https://mlsanity.com/paper/2512.15109/) (2025)
- [Efficient UAV trajectory prediction: A multi-modal deep diffusion framework](https://mlsanity.com/paper/2602.00107/) (2026)

## Abstract (arXiv)

Large-scale, precisely synchronized multi-modal datasets are critical for data-driven sixth-generation (6G) wireless research, yet real-world collection remains costly and difficult. Existing multi-platform simulators often suffer from timing misalignment, limited modality support, and simplified scene and material modeling, which reduce fidelity and enlarge the simulation-to-reality (Sim2Real) gap. We propose Great-X, a unified single-engine simulator implemented in Unreal Engine. Great-X integrates native ray tracing with co-located visual and electromagnetic material properties on shared 3D meshes, enabling pixel-level consistency between radio and visual outputs. A deterministic fixed-step clock and synchronous buffering provide frame-accurate alignment across CSI, RGB, depth, LiDAR, radar, and event data. Based on this framework, we construct Great-MCD, containing over three million synchronized samples across urban and rural environments, day and night conditions, multiple UAV types, and diverse trajectories. Experiments show that Great-X generates channel impulse responses consistent with real measurements and provides transferable data for wireless positioning and CSI feedback, outperforming existing simulators and measured-data baselines in both zero-shot and fine-tuned settings.