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.
Dynamic V2V channel measurement and modeling at street intersection scenarios
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A geometry-parameterized inference model for urban canyon radio channels is built from measurements and validated on second-order statistics.
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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 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.