A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
Analogical learning for cross- scenario generalization: Framework and application to intelligent lo- calization
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GCD extracts approximate geometric channel features from coarse scenario maps using ray tracing and neighborhood search, converts them to pseudo-channels via feature alignment, and fuses them with partial pilot estimates in a neural network to obtain complete CSI.
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Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations
A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
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Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt
GCD extracts approximate geometric channel features from coarse scenario maps using ray tracing and neighborhood search, converts them to pseudo-channels via feature alignment, and fuses them with partial pilot estimates in a neural network to obtain complete CSI.