  Channel prediction is an effective approach for reducing the feedback or
estimation overhead in massive multi-input multi-output (m-MIMO) systems.
However, existing channel prediction methods lack precision due to model
mismatch errors or network generalization issues. Large language models (LLMs)
have demonstrated powerful modeling and generalization abilities, and have been
successfully applied to cross-modal tasks, including the time series analysis.
Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered
channel prediction method (LLM4CP) to predict the future downlink channel state
information (CSI) sequence based on the historical uplink CSI sequence. We
fine-tune the network while freezing most of the parameters of the pre-trained
LLM for better cross-modality knowledge transfer. To bridge the gap between the
channel data and the feature space of the LLM, preprocessor, embedding, and
output modules are specifically tailored by taking into account unique channel
characteristics. Simulations validate that the proposed method achieves SOTA
prediction performance on full-sample, few-shot, and generalization tests with
low training and inference costs.
