Rain Rate Estimation Bounds and Weather-Adaptive Pilot Allocation for LEO Satellite ISAC
Haofan Dong, Houtianfu Wang, Hanlin Cai, O. Tansel Baydas, Ozgur B. Akan

TL;DR
This paper derives bounds for rain rate estimation from LEO satellite signals, proposes weather-adaptive pilot allocation, and validates models with extensive radar and gauge data.
Contribution
It introduces Bayesian bounds for rain estimation, a novel adaptive pilot scheme, and analyzes optimal satellite elevation for sensing performance.
Findings
Lower bound for rain rate detection at 0.95 mm/h with temporal correlation.
Weather-adaptive pilot allocation improves spectral efficiency under rain conditions.
Optimal sensing elevation identified at 15°, improving geometric sensing performance.
Abstract
Rain attenuates Ku-band satellite signals by up to 20~dB, encoding precipitation information along the Earth-space slant path. This paper derives the Bayesian Cram\'{e}r-Rao bound (BCRB) for rain rate estimation from LEO broadband OFDM downlinks. Using corrected ITU-R P.838-3 coefficients, the standard CRB yields a minimum detectable rain rate for a single link at the reference elevation. We derive the prior Fisher information in closed form for log-normal rain (, from 186{,}292 samples) and show that a single-snapshot BCRB reduces to ; exploiting temporal correlation () over a 30-min window further tightens it to , while multi-link fusion across links lowers the operating-point RMSE \emph{lower bound} at to approximately . Building on these bounds, we formulate…
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