Dataset for Sim2Real Meta-Learning-based Training for mmWave Beam Selection in V2X Networks

The dataset used in our SMART framework consists of two main components. The real-world e-FLASH dataset is a multimodal collection featuring synchronized LiDAR, camera, and GPS data captured in diverse mmWave V2X scenarios—including LOS, NLOS with pedestrian, static car, and moving car obstacles—totaling over 10,853 samples (∼22 GB processed data). The synthetic S-FLASH dataset is a high-fidelity digital twin recreation of the e-FLASH environment along a two-lane urban road. It is generated using open-source tools (Blender for image synthesis and Blensor for LiDAR simulation) combined with Wireless InSite for detailed ray-tracing, resulting in 26,600 samples (∼90 GB processed data). Together, these datasets enable robust training and evaluation for mmWave beam selection while facilitating effective synthetic-to-real domain adaptation via meta-learning.