TeRFS models dynamic radio fields via anisotropic spherical Gaussians bound to analytical temporal envelopes that enable explicit multipath birth-and-death, delivering 11.5% lower MSE and 6.9x faster training than baselines.
RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Prediction
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A unified parametric framework identifies active satellites and reconstructs RSS fields from measurements by linking beam geometry to spatial signal formation with adaptive complexity control.
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TeRFS: Temporal-Evolving Radio Field Synthesis
TeRFS models dynamic radio fields via anisotropic spherical Gaussians bound to analytical temporal envelopes that enable explicit multipath birth-and-death, delivering 11.5% lower MSE and 6.9x faster training than baselines.
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Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites
A unified parametric framework identifies active satellites and reconstructs RSS fields from measurements by linking beam geometry to spatial signal formation with adaptive complexity control.