发表机构
Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于MeanFlow的一步生成式CSI预测框架，通过信道信息先验初始化生成过程，仅需一次神经函数评估，在保持点预测精度的同时改善不确定性校准。
AI 中文摘要
信道状态信息（CSI）预测已成为缓解无线通信系统中信道老化问题的一种有前景的方法。近期，生成模型已被用于开发能够对未来信道实现的不确定性进行建模的CSI预测框架。然而，这些模型的高计算复杂度和推理延迟仍然是其实际部署的主要障碍。为解决这些挑战，本文聚焦于一类一步生成模型，即MeanFlow模型，其推理仅需一次神经函数评估（NFE），显著降低了计算成本和延迟。为缓解直接从噪声生成CSI的困难，我们引入了一种信道信息先验，该先验从未来信道的估计值而非纯高斯噪声初始化生成过程。融入这种信道信息先验显著提高了点预测精度和不确定性校准。此外，我们将所提出的基于MeanFlow的CSI预测框架与近期基于扩散的方法进行了比较。结果表明，MeanFlow在仅需一次NFE的情况下，实现了可比的点预测精度，同时生成了校准更好的CSI预测。
英文摘要
Channel state information (CSI) prediction has emerged as a promising approach for mitigating channel aging in wireless communication systems. Recently, generative models have been employed to develop CSI prediction frameworks capable of modeling the uncertainty in future channel realizations. However, the high computational complexity and inference latency of these models remain major obstacles to their practical deployment. To address these challenges, this paper focuses on a class of one-step generative models, namely MeanFlow models, in which inference requires only a single neural function evaluation (NFE), significantly reducing computational cost and latency. To alleviate the difficulty of generating CSI directly from noise, we introduce a channel-informed prior that initializes the generation process from an informative estimate of the future channel rather than pure Gaussian noise. Incorporating this channel-informed prior substantially improves both point prediction accuracy and uncertainty calibration. Furthermore, we compare the proposed MeanFlow-based CSI prediction framework with a recent diffusion-based approach. The results demonstrate that MeanFlow achieves comparable point prediction accuracy while producing better-calibrated CSI predictions with only a single NFE.
Commentssubmitted to IEEE WCNC 2027
