Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pre-trained to learn a universal channel representation for a wide range of communications and sensing tasks. While existing works on WCFM have demonstrated its great potentials in various downstream tasks, the models are all trained using perfect (i.e., error-free and complete) channel information state (CSI) data. In practical systems, however, only degraded CSI obtained from pilot-based channel estimation is accessible, leading to distorted channel representations and performance degradation in downstream tasks for some real-world environments with severe noise and interference. To address this issue, this paper proposes a new paradigm for WCFM, termed as Filter-and-Attend. In this paradigm, Filter refers to explicitly suppressing noise-plus-interference (NPI) in the received signals, while Attend means performing correlation-aware CSI completion and feature extraction using attention mechanism. Specifically, an enhanced WCFM architecture is developed. In this architecture, coarse estimates of the CSIs are first obtained and exploited to construct two projection matrices that extract NPI components in the received signals, which are further processed and removed by a subtraction module. The filtered signal is subsequently passed through a CSI completion network to get a clean CSI for feature extraction. Simulation results demonstrated that compared to the state-of-the-art solutions, WCFM with NPI suppression structure achieves improved performance on various downstream tasks including time-domain channel prediction, frequency-domain channel prediction, and localization.

翻译：无线信道基础模型（WCFM）是一种任务无关的AI模型，通过预训练学习适用于广泛通信与感知任务的通用信道表征。尽管现有关于WCFM的研究已证明其在多种下游任务中的巨大潜力，但这些模型均使用完美（即无误差且完整）的信道状态信息（CSI）数据进行训练。然而在实际系统中，仅能获取基于导频信道估计得到的退化CSI，这会导致信道表征失真，并在某些具有严重噪声和干扰的真实环境场景中引发下游任务性能下降。为解决此问题，本文提出了一种名为“滤波与注意力”的WCFM新范式。该范式中，“滤波”指显式抑制接收信号中的噪声加干扰（NPI）成分，“注意力”则指利用注意力机制执行相关性感知的CSI补全与特征提取。具体而言，本文设计了一种增强型WCFM架构。该架构首先获取CSI的粗估计，并利用其构建两个投影矩阵以提取接收信号中的NPI分量，再通过减法模块进一步处理并消除这些分量。滤波后的信号随后输入CSI补全网络以获取洁净的CSI进行特征提取。仿真结果表明，相较于现有最优方案，具备NPI抑制结构的WCFM在时域信道预测、频域信道预测及定位等多种下游任务中均实现了性能提升。