Channel foundation models assume access to fully observed channels, an assumption that fails in deployment. We introduce PilotWiMAE, a self-supervised framework whose encoder ingests noisy pilot observations directly and whose attention factorizes along the axis separating temporal from joint space-frequency processing, an inductive bias inspired by the physics of the problem. Pilot input shrinks the observation space by up to two orders of magnitude and also removes the unrealistic assumption of full-CSI availability while incurring lower latency. The factorized design generates robust representations by exploiting the separable channel structure and allows a pretraining mask ratio of $99\%$. We pair patch-normalized reconstruction, which captures small-scale fading structure, with an auxiliary scale loss that recovers the large-scale fading features, and use an AWGN curriculum to match pilot noise at pretraining and deployment. Pretrained solely on $3.5$\,GHz and evaluated at $28$\,GHz across in-distribution and out-of-distribution settings, PilotWiMAE's cross-frequency beam selection and channel characterization beat supervised baselines despite operating on a smaller observation space. To weaken the coupling between decoder capacity and representation quality, we further propose a decoder-centric pretraining stage following the encoder-decoder joint pretraining, which allows PilotWiMAE to demonstrate competitive channel estimation without sacrificing representation quality. To foster further work in this direction, we release the PilotWiMAE pretrained weights and training pipeline, together with CSIGen, our Sionna-based ray-tracing channel-generation tool, and the channel datasets used in this work.

翻译：信道基础模型假设可获取完全观测的信道，该假设在实际部署中并不成立。我们提出PilotWiMAE——一种自监督框架，其编码器直接处理含噪导频观测值，并采用沿时间轴与联合时空-频率处理轴分解的注意力机制，这一归纳偏置受问题物理特性启发。导频输入将观测空间缩小两个数量级，消除了全信道状态信息可用这一不切实际的假设，同时降低了延迟。分解设计通过利用可分离的信道结构生成鲁棒表示，并支持$99\%$的预训练掩码比例。我们将捕捉小尺度衰落结构的块归一化重构与恢复大尺度衰落特征的辅助尺度损失相结合，并采用AWGN课程学习策略在预训练与部署阶段匹配导频噪声。仅在$3.5$\,GHz预训练后，在$28$\,GHz的分布内与分布外场景下评估，PilotWiMAE的跨频波束选择与信道表征性能在更小观测空间上超越了有监督基线。为削弱解码器容量与表示质量之间的耦合，我们进一步提出在编码器-解码器联合预训练后加入解码器中心预训练阶段，使PilotWiMAE在保持表示质量的同时展现竞争力。为促进该方向后续研究，我们开源PilotWiMAE预训练权重与训练流程，以及基于Sionna的射线追踪信道生成工具CSIGen和本研究使用的信道数据集。