Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine

TL;DR

This paper introduces Great-X, a unified Unreal Engine-based platform for multimodal ISAC data simulation, enabling efficient data generation and a new UAV localization dataset with promising results.

Contribution

The paper presents Great-X, a novel single-engine platform for multimodal ISAC data simulation, and introduces the Great-MSD dataset along with a baseline localization algorithm.

Findings

Successful reconstruction of ray-tracing within Unreal Engine

Demonstrated the feasibility of CSI-based UAV 3D localization

Open-source dataset and code availability

Abstract

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.