Large Language Model Empowered CSI Feedback in Massive MIMO Systems

TL;DR

This paper introduces a novel framework leveraging large language models for efficient CSI feedback in massive MIMO systems by reformulating the task as a masked token prediction problem and optimizing mask selection based on self-information.

Contribution

It proposes a new LLM-based approach for CSI feedback, including a task reformulation and an information-theoretic mask selection strategy tailored for mMIMO systems.

Findings

Enhanced CSI feedback accuracy using LLMs.

Effective mask selection improves prediction performance.

Framework adaptable to various mMIMO configurations.

Abstract

Despite the success of large language models (LLMs) across domains, their potential for efficient channel state information (CSI) compression and feedback in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems remains largely unexplored yet increasingly important. In this paper, we propose a novel LLM-based framework for CSI feedback to exploit the potential of LLMs. We first reformulate the CSI compression feedback task as a masked token prediction task that aligns more closely with the functionality of LLMs. Subsequently, we design an information-theoretic mask selection strategy based on self-information, identifying and selecting CSI elements with the highest self-information at the user equipment (UE) for feedback. This ensures that masked tokens correspond to elements with lower self-information, while visible tokens correspond to elements with higher self-information, thus maximizing the accuracy of LLM predictions.