  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.
