Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine
This work addresses the need for efficient multimodal data simulation in ISAC, particularly for low-altitude UAV applications, though it appears incremental by building on existing engines like Unreal and Sionna.
The authors tackled the challenge of generating synchronized multimodal data for Integrated Sensing and Communication (ISAC) research by developing Great-X, a single-engine simulation platform that integrates ray-tracing and autonomous driving tools, resulting in the creation of an open-source dataset and a baseline algorithm for UAV 3D localization.
Scaling laws have achieved success in LLM and foundation models. To explore their potential in ISAC research, we propose Great-X. This single-engine multimodal data twin platform reconstructs the ray-tracing computation of Sionna within Unreal Engine and is deeply integrated with autonomous driving tools. This enables efficient and synchronized simulation of multimodal data, including CSI, RGB, Radar, and LiDAR. Based on this platform, we construct an open-source, large-scale, low-altitude UAV multimodal synaesthesia dataset named Great-MSD, and propose a baseline CSI-based UAV 3D localization algorithm, demonstrating its feasibility and generalizability across different CSI simulation engines. The related code and dataset will be made available at: https://github.com/hkw-xg/Great-MCD.