Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models' strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt-enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. To further enhance reconstruction quality, the channel distribution (environmental knowledge) -- represented as the mean of channel magnitude in the angular domain -- is incorporated as a prompt within the decoder. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.

翻译：人工智能已成为信道状态信息反馈的一种有前景的工具。尽管当前研究主要致力于通过新颖的架构提升特定数据集上的反馈精度，但基于AI的CSI反馈的内在机制仍不明确。本研究通过分析模型在不同数据集上的性能来探究其机制，发现优异的反馈性能源于AI模型强大的拟合能力及其对环境知识的利用。基于这些发现，我们提出了一种支持提示功能的大型AI模型用于CSI反馈。该模型采用强大的Transformer模块，并在多场景的大规模数据集上进行训练。为进一步提升重建质量，我们将信道分布（环境知识）——表现为角度域中信道幅度的均值——作为提示信息嵌入解码器。仿真结果表明，所提出的支持提示的大型AI模型能显著提升反馈精度与泛化性能，同时降低新场景下的数据采集需求。