Problem
Research questions and friction points this paper is trying to address.
Rainfall Sensing
Mobile Communication Signals
Sub-6 GHz
Infrastructure Cost
Signal Attenuation
Rainfall Sensing via Mobile Communication Signals
🤖 AI Summary
This study addresses the high cost of conventional rain gauges and the weak rain attenuation in Sub-6GHz signals by proposing PMN-RainSense, a novel framework for precise rainfall retrieval under single-antenna constraints. Overcoming the limitations of traditional attenuation-based methods, this approach leverages spectral-temporal Channel State Information (CSI) compensation and delay-Doppler domain features, integrated with angular filtering and deep learning models. Experimental evaluations demonstrate that the framework achieves 95.48% accuracy in three-class WiFi rainfall classification and a Mean Absolute Error (MAE) of 0.25–0.27 mm/h in real-world LTE base station rainfall intensity estimation. These results validate the effectiveness of utilizing fine-grained CSI from mobile communication systems for low-cost, high-precision rainfall sensing, offering a scalable alternative to dedicated meteorological infrastructure.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sub-6GHz Rainfall Sensing
Spectral-Temporal CSI Compensation
Delay-Doppler Feature Extraction
Doppler Broadening
Single-Antenna Deployment
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