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.
Channel estimation and symbol demod- ulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks
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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.