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arXiv · 2507.08716

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

Yuan Gao·Kongwu Huang·Jun Jiang·Shiyi Mu·Shugong Xu
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Abstract

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.

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Yuan Gao, Kongwu Huang, Jun Jiang, Shiyi Mu, Shugong Xu. 2025-07-11. Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G. https://arxiv.org/abs/2507.08716

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