A dual-branch cross-attention neural network with recurrent tracking reconstructs complete channel impulse responses from satellite imagery by predicting TDL parameters, reaching over 0.96 PDP cosine similarity on unseen sites.
3GPP TR 38.901 channel model
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A new site-specific model uses 3D geometry maps and recursive UTD diffraction calculations to predict urban radio path loss and time-varying Doppler more accurately than 3GPP models, with RMSE reductions of 7.1 dB in complex NLOS cases.
A geometry-parameterized inference model for urban canyon radio channels is built from measurements and validated on second-order statistics.
citing papers explorer
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Deep Learning-Based Site-Specific Channel Modeling and Inference
A dual-branch cross-attention neural network with recurrent tracking reconstructs complete channel impulse responses from satellite imagery by predicting TDL parameters, reaching over 0.96 PDP cosine similarity on unseen sites.
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A Geometry Map-Based Site-Specific Propagation Channel Model for Urban Scenarios
A new site-specific model uses 3D geometry maps and recursive UTD diffraction calculations to predict urban radio path loss and time-varying Doppler more accurately than 3GPP models, with RMSE reductions of 7.1 dB in complex NLOS cases.
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A Novel Site-Specific Inference Model for Urban Canyon Channels: From Measurements to Modeling
A geometry-parameterized inference model for urban canyon radio channels is built from measurements and validated on second-order statistics.