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 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.