[2604.01944] Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

[2604.01944] Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2604.01944: Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

Computer Science > Networking and Internet Architecture arXiv:2604.01944 (cs) [Submitted on 2 Apr 2026] Title:Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction Authors:Anatolij Zubow, Joana Angjo, Sigrid Dimce, Falko Dressler View a PDF of the paper titled Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction, by Anatolij Zubow and 3 other authors View PDF HTML (experimental) 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. Mob...

Originally published on April 03, 2026. Curated by AI News.

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