AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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2025

Yang, T.; Zhang, P.; Zheng, M.; Li, N.; Ma, S.

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

In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331543709 (ISBN).

Abstract | Links | BibTeX | Tags: Environment reconstruction, Generative model, Generative pretrained transformer, High-fidelity, Imaging problems, Integrated sensing, Integrated sensing and communication, Metaverses, Mobile telecommunication systems, Signal processing, Three dimensional computer graphics, Wireless environment, Wireless environment reconstruction

@inproceedings{yang_envrecongpt_2025,

title = {EnvReconGPT: A Generative AI Model for Wireless Environment Reconstruction in the 6G Metaverse},

author = {T. Yang and P. Zhang and M. Zheng and N. Li and S. Ma},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017962023&doi=10.1109%2FINFOCOMWKSHPS65812.2025.11152849&partnerID=40&md5=3d776e0774732f91291ba6ff90078957},

doi = {10.1109/INFOCOMWKSHPS65812.2025.11152849},

isbn = {9798331543709 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {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. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Environment reconstruction, Generative model, Generative pretrained transformer, High-fidelity, Imaging problems, Integrated sensing, Integrated sensing and communication, Metaverses, Mobile telecommunication systems, Signal processing, Three dimensional computer graphics, Wireless environment, Wireless environment reconstruction},

pubstate = {published},

tppubtype = {inproceedings}

}