Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain CSI data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.

翻译：精确的信道状态信息获取对于现代无线系统至关重要，而在大规模天线阵列、严格的导频开销约束以及多样化部署环境下，这一任务变得日益困难。现有基于人工智能的解决方案往往缺乏鲁棒性，且难以跨场景泛化。为应对这一局限，本文提出了一种基于预测基础模型的信道估计框架，能够实现精确、低开销且可泛化的信道状态信息获取。所提框架采用在大规模跨域信道状态信息数据上训练的预测基础模型，以提取通用信道表征并提供具有强跨场景可迁移性的预测先验。进一步设计了基于视觉Transformer架构的导频处理网络，以从导频观测中捕获空间、时间和频率相关性。一种高效的融合机制将预测先验与实时测量相结合，即使在稀疏或噪声条件下也能实现可靠的信道状态信息重建。多种配置下的广泛评估表明，所提出的估计器在精度、鲁棒性和泛化能力方面均显著优于经典方法和数据驱动的基线模型。