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

## TL;DR

This paper introduces LLM4FB, a framework using large language models to reduce feedback overhead in wireless systems while maintaining accuracy.

## Contribution

LLM4FB uses pre-trained large language models to enable low-complexity CSI feedback and prediction with minimal UE computation.

## Key findings

- LLM4FB achieves high-precision CSI feedback with minimal computational burden on user equipment.
- The framework outperforms existing methods across various compression ratios and mobility levels.
- A multiobjective loss function optimizes both NMSE and spectral efficiency effectively.

## Abstract

Massive MIMO systems can substantially enhance spectral efficiency, but such gains rely on the availability of accurate channel state information (CSI). However, the increase in the number of antennas leads to a significant growth in feedback overhead, while conventional deep-learning-based CSI feedback methods also impose a substantial computational burden on the user equipment (UE). To address these challenges, this paper proposes LLM4FB, a one-sided CSI feedback framework that leverages a pre-trained large language model (LLM). In this framework, the UE performs only low-complexity linear projections to compress CSI. In contrast, the BS leverages a pre-trained LLM to accurately reconstruct and predict CSI. By utilizing the powerful modeling capabilities of the pre-trained LLM, only a small portion of the parameters needs to be fine-tuned to improve CSI recovery accuracy with low training cost. Furthermore, a multiobjective loss function is designed to simultaneously optimize normalized mean square error (NMSE) and spectral efficiency (SE). Simulation results show that LLM4FB outperforms existing methods across various compression ratios and mobility levels, achieving high-precision CSI feedback with minimal computational capability from terminal devices. Therefore, LLM4FB presents a highly promising solution for next-generation wireless sensor networks and industrial IoT applications, where terminal devices are often strictly constrained by energy and hardware resources.