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Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin, En Tong

TL;DR

This work addresses the overhead of downlink CSI feedback in massive MIMO by introducing a pre-trained large language model (LLM) framework for CSI reconstruction. It combines a four-module pipeline—pre-processing, embedding, a pre-trained GPT-2, and post-processing—tuned with minimal parameter updates to leverage language priors while bridging CSI data to textual representations. Empirical results show the LLM-based approach outperforms small DL baselines and TransNet in NMSE and GCS, with stronger gains at higher compression ratios and robust generalization with limited training data. The findings demonstrate a cost-efficient, scalable pathway to intelligent CSI feedback that exploits knowledge learned from natural language processing to enhance wireless system performance.

Abstract

Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs -- capable of error correction in language tasks -- to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction.

Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

TL;DR

This work addresses the overhead of downlink CSI feedback in massive MIMO by introducing a pre-trained large language model (LLM) framework for CSI reconstruction. It combines a four-module pipeline—pre-processing, embedding, a pre-trained GPT-2, and post-processing—tuned with minimal parameter updates to leverage language priors while bridging CSI data to textual representations. Empirical results show the LLM-based approach outperforms small DL baselines and TransNet in NMSE and GCS, with stronger gains at higher compression ratios and robust generalization with limited training data. The findings demonstrate a cost-efficient, scalable pathway to intelligent CSI feedback that exploits knowledge learned from natural language processing to enhance wireless system performance.

Abstract

Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs -- capable of error correction in language tasks -- to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction.
Paper Structure (17 sections, 15 equations, 5 figures)

This paper contains 17 sections, 15 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. LLM for CSI Feedback
  4. Motivation
  5. Key Idea
  6. Pre-processing
  7. Embedding
  8. Pre-trained LLM
  9. Post-processing
  10. Simulation Results
  11. Simulation Settings
  12. Channel Generation Settings
  13. Training Settings
  14. Performance Improvement
  15. Training Cost Reduction
  16. ...and 2 more sections

Figures (5)

  • Figure 1: (a) An example of LLM-based sentence correction. (b) One-sided DL-based CSI feedback, primarily utilizing a neural network at the BS for CSI reconstruction. (c) Comparison between LLM-based sentence correction and CSI reconstruction.
  • Figure 2: Detailed architecture of the proposed LLM-based CSI feedback method, consisting of pre-processing, embedding, pre-trained LLM, and post-processing modules.
  • Figure 3: Comparison of the pre-trained LLM-based CSI feedback method and baseline methods with 50 scenarios and 10,000 samples per scenario.
  • Figure 4: Comparison of the pre-trained LLM-based CSI feedback method and baseline methods under limited training data conditions, with 50 scenarios used for evaluation.
  • Figure 5: Comparison of the generalization capability between the pre-trained LLM-based CSI feedback method and baseline methods. The models are trained on the mixed dataset from scenarios indexed 1 to 50 and tested on the mixed dataset from scenarios indexed 51 to 100, with 10,000 samples per scenario.