基于移动通信信号的降雨感知
Rainfall Sensing via Mobile Communication Signals
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中文总结 AI 辅助
针对传统降雨监测设备的缺陷，提出PMN-RainSense框架，利用sub-6 GHz移动通信信号，经实验实现降雨分类95.48%准确率、强度估计MAE 0.25-0.27 mm/h的性能。
中文摘要 AI 辅助
降雨监测对于水文观测、灾害预警和环境感知具有重要意义，但传统雨量计和天气雷达存在部署稀疏、基础设施成本高的问题。本文提出PMN-RainSense，一种使用sub-6 GHz移动通信信号的降雨感知框架，支持实用的单天线部署。与基于衰减的方法不同，后者在sub-6 GHz频段不可靠，因为移动接入短链路上的降雨引起的衰减仅为百分之一分贝量级，所提框架利用细粒度动态特性。一种频谱-时间信道状态信息（CSI）补偿方法抑制逐包定时和相位失真，同时保留与感知相关的信息。从延迟-多普勒域提取降雨敏感特征以减轻环境干扰，角度域滤波作为多天线接收机的可选扩展。在带宽和天线约束下，与降雨相关的多普勒波动作为主要感知特征，而多普勒域归一化提高了链路和部署间的鲁棒性。受控WiFi实验证明了降雨相关的多普勒展宽，并使用随机森林分类器实现了95.48%的三类分类准确率。从蜂窝基站收集的0.763至2.68 GHz范围内11个载波频率的长期演进（LTE）CSI测量，使用一维卷积网络进行降雨强度估计，平均绝对误差（MAE）为0.25-0.27 mm/h。
英文摘要
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.
