LLM4CP: Adapting Large Language Models for Channel Prediction This paper proposes a novel method, LLM4CP, for channel prediction in massive multi-input multi-output (m-MIMO) systems. Channel prediction is crucial for reducing feedback and estimation overhead in m-MIMO systems. Existing methods face challenges due to model mismatch and generalization issues. LLMs, known for their strong modeling and generalization abilities, are applied to channel prediction tasks. LLM4CP uses a pre-trained GPT-2 model, fine-tuned for channel prediction, to predict future downlink channel state information (CSI) based on historical uplink CSI. The method includes a preprocessor, embedding, and output module tailored to the unique characteristics of channel data. Simulations show that LLM4CP achieves state-of-the-art performance in full-sample, few-shot, and generalization tests with low training and inference costs. The paper introduces a channel prediction-based transmission scheme, which predicts future downlink CSI based on historical uplink CSI. This approach avoids the overhead of downlink pilots and feedback delay. The problem formulation involves predicting future downlink CSI based on historical CSI. The proposed method uses a pre-trained LLM to model complex time-frequency relationships, with specific modules for format conversion and feature extraction. The preprocessor converts CSI data into real tensors, and the embedding module extracts features using CSI attention and position encoding. The backbone network is a pre-trained LLM, with most parameters frozen during training. The output module converts the LLM's output into the final prediction results. Experiments show that LLM4CP outperforms existing methods in terms of prediction accuracy, robustness against noise, few-shot learning, and generalization. The method achieves high performance across different user velocities and channel conditions, demonstrating its effectiveness in both TDD and FDD systems. The training and inference costs are low, making the method suitable for practical deployment. The paper also evaluates the impact of different numbers of GPT-2 layers on performance, showing that 6 layers provide the best results. The proposed method has strong generalization capabilities and can be applied to different frequency bands and channel scenarios. Future work includes exploring more comprehensive experimental setups and validating the method with more realistic datasets.LLM4CP: Adapting Large Language Models for Channel Prediction This paper proposes a novel method, LLM4CP, for channel prediction in massive multi-input multi-output (m-MIMO) systems. Channel prediction is crucial for reducing feedback and estimation overhead in m-MIMO systems. Existing methods face challenges due to model mismatch and generalization issues. LLMs, known for their strong modeling and generalization abilities, are applied to channel prediction tasks. LLM4CP uses a pre-trained GPT-2 model, fine-tuned for channel prediction, to predict future downlink channel state information (CSI) based on historical uplink CSI. The method includes a preprocessor, embedding, and output module tailored to the unique characteristics of channel data. Simulations show that LLM4CP achieves state-of-the-art performance in full-sample, few-shot, and generalization tests with low training and inference costs. The paper introduces a channel prediction-based transmission scheme, which predicts future downlink CSI based on historical uplink CSI. This approach avoids the overhead of downlink pilots and feedback delay. The problem formulation involves predicting future downlink CSI based on historical CSI. The proposed method uses a pre-trained LLM to model complex time-frequency relationships, with specific modules for format conversion and feature extraction. The preprocessor converts CSI data into real tensors, and the embedding module extracts features using CSI attention and position encoding. The backbone network is a pre-trained LLM, with most parameters frozen during training. The output module converts the LLM's output into the final prediction results. Experiments show that LLM4CP outperforms existing methods in terms of prediction accuracy, robustness against noise, few-shot learning, and generalization. The method achieves high performance across different user velocities and channel conditions, demonstrating its effectiveness in both TDD and FDD systems. The training and inference costs are low, making the method suitable for practical deployment. The paper also evaluates the impact of different numbers of GPT-2 layers on performance, showing that 6 layers provide the best results. The proposed method has strong generalization capabilities and can be applied to different frequency bands and channel scenarios. Future work includes exploring more comprehensive experimental setups and validating the method with more realistic datasets.