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CSI-GPT: Integrating Generative Pre-Trained Transformer With Federated-Tuning to Acquire Downlink Massive MIMO Channels
Zeng, Ye Qiao, Li Gao, Zhen Qin, Tong Wu, Zhonghuai Khalaf, Emad Guizani, Mohsen
Zeng, Ye
Qiao, Li
Gao, Zhen
Qin, Tong
Wu, Zhonghuai
Khalaf, Emad
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
In massive multiple-input multiple-output (MIMO) systems, how to reliably acquire downlink channel state information (CSI) with low overhead is challenging. In this work, by integrating the generative pre-trained Transformer (GPT) with federated-tuning, we propose a CSI-GPT approach to realize efficient downlink CSI acquisition. Specifically, we first propose a Swin Transformer-based channel acquisition network (SWTCAN) to acquire downlink CSI, where pilot signals, downlink channel estimation, and uplink CSI feedback are jointly designed. Furthermore, to solve the problem of insufficient training data, we propose a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell. The CSI dataset generated from VAE-CSG will be used for pre-training SWTCAN. To fine-tune the pre-trained SWTCAN for improved performance, we propose an online federated-tuning method, where only a small amount of SWTCAN parameters are unfrozen and updated using over-the-air computation, avoiding the high communication overhead caused by aggregating the complete CSI samples from user equipment (UEs) to the BS for centralized fine-tuning. Simulation results verify the advantages of the proposed SWTCAN and the communication efficiency of the proposed federated-tuning method.
Citation
Y. Zeng et al., “CSI-GPT: Integrating Generative Pre-Trained Transformer With Federated-Tuning to Acquire Downlink Massive MIMO Channels,” IEEE Trans Veh Technol, vol. 74, no. 3, pp. 5187–5192, Mar. 2025, doi: 10.1109/TVT.2024.3493463.
Source
IEEE Transactions on Vehicular Technology
Conference
Keywords
Channel estimation, CSI feedback, federated learning, generative AI, massive MIMO, swin transformer
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Source
Publisher
IEEE