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

TL;DR

This paper explores using large language models for compressing and reconstructing channel state information in massive MIMO systems, demonstrating promising initial results for wireless communication enhancement.

Contribution

It introduces a novel framework that adapts LLMs for CSI feedback, including customized processing modules, to improve wireless signal reconstruction.

Findings

LLMs show potential in CSI compression and error correction.

Customized processing enhances LLM adaptation to wireless signals.

Numerical results indicate promising performance improvements.

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.