A closed-form weighted least-squares estimator plus a learned-residual neural network jointly estimates 3D user position, velocity, and single-bounce scatterer locations in millimeter-wave cloud radio access networks.
Mm-wave MIMO channel modeling and user localization using sparse beamspace signatures,
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3-D Positioning and Environment Mapping for mmWave Communication Systems
A closed-form weighted least-squares estimator plus a learned-residual neural network jointly estimates 3D user position, velocity, and single-bounce scatterer locations in millimeter-wave cloud radio access networks.