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.
Deep learning-based channel estimation
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HyperCEUNet improves wireless channel estimation accuracy by using a hypernetwork to adapt a UNet's front-end layer based on estimated channel parameters and Wiener-filter initialization.
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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.
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HyperCEUNet: Parameter-Aware Hypernetwork-Driven UNet for Channel Estimation
HyperCEUNet improves wireless channel estimation accuracy by using a hypernetwork to adapt a UNet's front-end layer based on estimated channel parameters and Wiener-filter initialization.