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

arXiv: https://arxiv.org/abs/2501.10630 · HTML: https://mlsanity.com/paper/2501.10630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G](https://mlsanity.com/paper/2605.00020/) (2026)
- [MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction](https://mlsanity.com/paper/2604.21957/) (2026)
- [A Wireless World Model for AI-Native 6G Networks](https://mlsanity.com/paper/2603.25216/) (2026)
- [REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models](https://mlsanity.com/paper/2609.19903/) (2026)

## Abstract (arXiv)

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.