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

Shilong Fan, Zhenyu Liu, Xinyu Gu, Haozhen Li

TL;DR

This work introduces Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences and aligns 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).

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.

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

TL;DR

This work introduces Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences and aligns 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).

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.
Paper Structure (18 sections, 5 equations, 3 figures, 3 tables)

This paper contains 18 sections, 5 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Model and CSI Prediction
  3. Massive MIMO OFDM Systems
  4. Downlink CSI Prediction
  5. Csi-LLM
  6. Network Architecture Alignment
  7. Data Processing Alignment
  8. Optimization objectives Alignment
  9. Experiment
  10. Experimental Settings
  11. Datasets
  12. Performance Indices
  13. Training Setup
  14. Prediction Performance
  15. One-step Prediction
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Csi-LLM network architecture overview.(The solid line represents the forward propagation of Csi-LLM, while the dashed line indicates the comparison for the text task.)
  • Figure 2: The comparative experimental results of continuous autoregressive downlink channel prediction.
  • Figure 3: The impact of language foundation models on the downlink channel prediction performance of Csi-LLM.