Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

• URL: http://arxiv.org/abs/2604.01944v1
• Date: Thu, 02 Apr 2026 12:06:27 GMT
• Title: Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction
• Abstract summary: We present a physics-informed complex Transformer that reconstructs the full wideband frequency response.<n>The interference pattern in each sub-band is modeled as an independent two-state discrete-time Markov chain.<n>The model degrades smoothly across the full velocity range, consistently outperforming all other baselines.
• Score: 12.10905157982893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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