Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training

TL;DR

Csi-LLM introduces a large language model-based approach for downlink channel prediction in massive MIMO systems, effectively modeling variable-step sequences and aligning with NLP techniques to improve accuracy and scalability.

Contribution

This work presents a novel LLM-powered method for downlink channel prediction that models variable-step sequences and aligns training with natural language processing tasks.

Findings

Csi-LLM outperforms traditional methods in stability and accuracy.

Effective cross-modality alignment enhances multi-step prediction.

Simulation results show significant performance improvements.

Abstract

Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality, and scalability of channel prediction. Recent advances have shown that large language models (LLMs) exhibit strong pattern recognition and reasoning abilities over complex sequences. The challenge lies in effectively aligning wireless communication data with the modalities used in natural language processing to fully harness these capabilities. In this work, we introduce Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences. To ensure effective cross-modality application, we align the design and training of Csi-LLM with the processing of natural language tasks, leveraging the LLM's next-token generation capability for predicting the next step in channel state information (CSI). Simulation results demonstrate the effectiveness of this alignment strategy, with Csi-LLM consistently delivering stable performance improvements across various scenarios and showing significant potential in continuous multi-step prediction.