Wideband channel frequency response (CFR) estimation is challenging in multi-band wireless systems, especially when one or more sub-bands are temporarily blocked by co-channel interference. We present a physics-informed complex Transformer that reconstructs the full wideband CFR from such fragmented, partially observed spectrum snapshots. The interference pattern in each sub-band is modeled as an independent two-state discrete-time Markov chain, capturing realistic bursty occupancy behavior. Our model operates on the joint time-frequency grid of $T$ snapshots and $F$ frequency bins and uses a factored self-attention mechanism that separately attends along both axes, reducing the computational complexity to $O(TF^2 + FT^2)$. Complex-valued inputs and outputs are processed through a holomorphic linear layer that preserves phase relationships. Training uses a composite physics-informed loss combining spectral fidelity, power delay profile (PDP) reconstruction, channel impulse response (CIR) sparsity, and temporal smoothness. Mobility effects are incorporated through per-sample velocity randomization, enabling generalization across different mobility regimes. Evaluation against three classical baselines, namely, last-observation-carry-forward, zero-fill, and cubic-spline interpolation, shows that our approach achieves the highest PDP similarity with respect to the ground truth, reaching $ρ\geq 0.82$ compared to $ρ\geq 0.62$ for the best baseline at interference occupancy levels up to 50%. Furthermore, the model degrades smoothly across the full velocity range, consistently outperforming all other baselines.

翻译：宽带信道频率响应（CFR）估计在多频带无线系统中具有挑战性，尤其当一个或多个子频带被同信道干扰暂时阻塞时。我们提出了一种物理知识引导的复数Transformer，用于从这种碎片化、部分观测的频谱快照中重建完整的宽带CFR。每个子频带中的干扰模式被建模为独立的两状态离散时间马尔可夫链，捕捉实际的突发占用行为。我们的模型在$T$个快照和$F$个频率点的时频联合网格上运行，采用分解自注意力机制，分别沿两个轴进行注意力计算，将计算复杂度降低至$O(TF^2 + FT^2)$。复数值输入和输出通过保持相位关系的全纯线性层处理。训练使用复合物理知识引导损失函数，结合频谱保真度、功率延迟分布（PDP）重建、信道冲激响应（CIR）稀疏性和时间平滑性。通过逐样本速度随机化引入移动性效应，实现跨不同移动场景的泛化。与三种经典基线方法（即最近观测前向填充、零填充和三次样条插值）的评估对比表明，我们的方法在PDP相似度上与真实值最为接近，在干扰占用水平高达50%时达到$ρ\geq 0.82$，而最佳基线的$ρ\geq 0.62$。此外，模型在整个速度范围内性能稳定下降，始终优于所有其他基线方法。