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LLM4FB: A One-Sided CSI Feedback and Prediction Framework for Lightweight UEs via Large Language Models.

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Summary

This study introduces LLM4FB, a novel framework for efficient channel state information (CSI) feedback in Massive MIMO systems. LLM4FB significantly reduces feedback overhead and computational load on user equipment using large language models (LLMs).

Keywords:
CSI feedbackchannel predictionindustrial IoTlarge language modelmassive MIMO

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Artificial intelligence

Background:

  • Massive MIMO enhances spectral efficiency but requires accurate channel state information (CSI).
  • High antenna counts increase feedback overhead, and current deep learning methods burden user equipment (UE) computationally.
  • Existing CSI feedback methods struggle with efficiency and UE resource constraints.

Purpose of the Study:

  • To propose LLM4FB, a novel one-sided CSI feedback framework utilizing a pre-trained large language model (LLM).
  • To reduce feedback overhead and computational complexity for UEs in Massive MIMO systems.
  • To enhance CSI reconstruction accuracy and spectral efficiency with minimal UE resources.

Main Methods:

  • The UE employs low-complexity linear projections for CSI compression.
  • A pre-trained LLM at the base station (BS) reconstructs and predicts CSI.
  • A multi-objective loss function optimizes Normalized Mean Square Error (NMSE) and Spectral Efficiency (SE).

Main Results:

  • LLM4FB achieves high-precision CSI feedback across various compression ratios and mobility levels.
  • The framework outperforms existing CSI feedback methods.
  • Minimal fine-tuning of LLM parameters is required, reducing training costs.

Conclusions:

  • LLM4FB offers a promising solution for CSI feedback in resource-constrained environments.
  • The framework is suitable for next-generation wireless sensor networks and industrial IoT.
  • LLM4FB effectively addresses the challenges of feedback overhead and UE computational burden.