AI 中文总结
针对有限比特CSI反馈中细粒度信道恢复难题，提出CoFi-CLM模型，在基站端利用Transformer预测细粒度令牌并融合粗粒度重建，在64-160比特下性能优于现有方法，128比特时NMSE提升约1dB。
AI 中文摘要
在频分双工大规模多输入多输出（MIMO）系统中，用户设备（UE）必须在严格的有限比特反馈预算下传输高维下行信道状态信息（CSI）。深度学习（DL）因其能够捕获复杂的信道相关性并学习紧凑的CSI表示，已成为CSI压缩的强大工具。然而，从有限的反馈中恢复细粒度的信道结构仍然具有挑战性。为了解决这一挑战，我们提出了从粗到细信道语言模型（CoFi-CLM），一种部署在基站（BS）的大型AI模型，该模型显式学习粗粒度与细粒度CSI表示之间的依赖关系。具体而言，UE从学习到的码本中报告粗粒度令牌索引，而CoFi-CLM在单次Transformer前向传播中，基于该反馈预测未报告的细粒度令牌索引上的分布。一个双路径解码器将直接的粗粒度重建与生成的细粒度重建相融合。将细粒度令牌预测和融合集中在BS端，使得CLM的容量可以扩展，而不会增加UE的计算或存储成本。在64到160个反馈比特范围内，CoFi-CLM在已见和未见场景下均持续优于对比方法。在128比特时，它在两组数据上相较于近期的大型AI模型基线，将归一化均方误差（NMSE）提高了约1 dB。已见与未见场景之间的性能差距进一步支持了其在所考虑信道模型下的泛化能力。源代码可在此https URL公开获取。
英文摘要
In frequency-division duplex massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) must convey high-dimensional downlink channel state information (CSI) under a stringent finite-bit feedback budget. Deep learning (DL) has emerged as a powerful tool for CSI compression due to its ability to capture complex channel correlations and learn compact CSI representations. However, recovering fine-scale channel structure from limited feedback remains challenging. To address this challenge, we propose the Coarse-to-Fine Channel Language Model (CoFi-CLM), a large AI model deployed at the base station (BS) that explicitly learns dependencies between coarse and fine CSI representations. Specifically, the UE reports coarse-token indices from a learned codebook, while CoFi-CLM predicts distributions over unreported fine-token indices conditioned on this feedback in a single Transformer forward pass. A dual-path decoder fuses the direct coarse reconstruction with the generated fine-scale reconstruction. Concentrating fine-token prediction and fusion at the BS allows the CLM capacity to scale without increasing the computational or storage cost at the UE. Across 64 to 160 feedback bits, CoFi-CLM consistently outperforms the compared methods on both seen and unseen scenarios. At 128 bits, it improves the normalized mean square error (NMSE) over the recent large AI model baseline by approximately 1 dB on both sets. The performance gap between seen and unseen scenarios further supports its generalization under the considered channel model. The source code is publicly available at https://github.com/doovvv/CoFi-CLM.
