AI summaryⓘ
The authors developed a method called PMN-RainSense that uses everyday mobile communication signals (below 6 GHz) to detect rainfall, avoiding the need for expensive and sparsely placed rain gauges or radars. Instead of relying on how rain blocks signals (which is barely noticeable at these frequencies), their system looks at subtle changes in signal patterns caused by rain. They use advanced processing to clean up signal data and identify rain effects through Doppler shifts, which correlate well with rainfall. Tests using WiFi and cellular signals showed that their approach could classify rain types accurately and estimate rain intensity with low error. This suggests a new, practical way to monitor rain using existing mobile networks.
Rainfall SensingSub-6 GHz SignalsChannel State Information (CSI)Doppler EffectRandom Forest ClassifierLTE (Long-Term Evolution)Delay-Doppler DomainSignal AttenuationConvolutional NetworkAngle-Domain Filtering
Authors
Zhongqin Wang, J. Andrew Zhang, Kai Wu, Y. Jay Guo
Abstract
Rainfall monitoring is important for hydrological observation, disaster warning, and environmental sensing, but conventional rain gauges and weather radars suffer from sparse deployment and high infrastructure costs. This paper proposes PMN-RainSense, a rainfall sensing framework using sub-6-GHz mobile communication signals that supports practical single-antenna deployment. Unlike attenuation-based approaches, which are unreliable at sub-6 GHz because rain-induced attenuation over short mobile access links is only on the order of hundredths of a decibel, the proposed framework exploits fine-grained dynamics. A spectral-temporal channel state information (CSI) compensation method suppresses packet-wise timing and phase distortions while preserving sensing-relevant information. Rainfall-sensitive features are extracted from the delay-Doppler domain to mitigate environmental interference, with angle-domain filtering as an optional extension for multi-antenna receivers. Under bandwidth and antenna constraints, rainfall-correlated Doppler fluctuations serve as the dominant sensing signature, while Doppler-domain normalization improves robustness across links and deployments. Controlled WiFi experiments demonstrate rainfall-associated Doppler broadening and achieve a three-class classification accuracy of 95.48% using a random forest classifier. Long-Term Evolution (LTE) CSI measurements collected from cellular base stations over 11 carrier frequencies from 0.763 to 2.68 GHz yield a mean absolute error (MAE) of 0.25-0.27 mm/h for rainfall intensity estimation using a one-dimensional convolutional network.