Anchor-CKM reconstructs channel knowledge maps from sparse irregular measurements by constructing a pilot-supported representation with partial convolutions followed by layout-conditioned Fourier refinement, achieving 0.79-1.33 dB RMSE reduction versus baselines on DeepMIMO scenarios.
Fourier features let networks learn high frequency functions in low dimensional domains
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Unsupervised SIREN-based online fitting with physics-aware loss enables robust channel estimation for high-mobility OFDM, outperforming LS and LMMSE in V2X simulations with good OOD generalization.
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Channel Knowledge Map Reconstruction From Sparse Measurements via Pilot-Anchored Layout-Conditioned Fourier Refinement
Anchor-CKM reconstructs channel knowledge maps from sparse irregular measurements by constructing a pilot-supported representation with partial convolutions followed by layout-conditioned Fourier refinement, achieving 0.79-1.33 dB RMSE reduction versus baselines on DeepMIMO scenarios.
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Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation
Unsupervised SIREN-based online fitting with physics-aware loss enables robust channel estimation for high-mobility OFDM, outperforming LS and LMMSE in V2X simulations with good OOD generalization.