EnvReconGPT: A Generative AI Model for Wireless Environment Reconstruction in the 6G Metaverse

This study introduces EnvReconGPT, a Transformer-based generative model specifically designed for wireless environment reconstruction in the 6G Metaverse, with a parameter size of 3.5 billion. The model leverages the generative capabilities of Transformers to frame wireless environment reconstruction as an imaging problem, treating it as a task of generating high-fidelity 3D point clouds. A novel multimodal mechanism is proposed to fuse base station positional information with Channel Frequency Response data, enabling the model to fully capture the spatial and spectral characteristics of the environment. Additionally, a feature embedding method is developed to integrate these multimodal inputs into the Transformer architecture effectively. By employing the Chamfer Distance as the loss function, EnvReconGPT ensures precise geometric alignment between predicted and ground truth point clouds, achieving robust performance across diverse scenarios. This work highlights the potential of generative AI in advancing ISAC systems and enabling wireless systems to meet the demands of the 6G Metaverse.