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.

翻译：尽管大规模语言模型（LLMs）在各个领域取得了成功，但其在频分双工（FDD）大规模多输入多输出（mMIMO）系统中用于高效信道状态信息（CSI）压缩与反馈的潜力，虽日益重要却仍未得到充分探索。本文提出了一种新颖的基于LLM的CSI反馈框架，以挖掘LLMs的潜力。我们首先将CSI压缩反馈任务重新表述为一个掩码令牌预测任务，使其更贴近LLMs的功能特性。随后，我们设计了一种基于自信息的信息论掩码选择策略，在用户设备（UE）端识别并选择具有最高自信息的CSI元素进行反馈。这确保了掩码令牌对应于自信息较低的元素，而可见令牌对应于自信息较高的元素，从而最大化LLM预测的准确性。