# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 1 Introduction  
Environmental changes, particularly meteorological disasters, significantly impact human safety, infrastructure, and ecosystems. Timely and accurate monitoring is essential for effective disaster management and damage mitigation. Environmental sensing plays a key role in this process, covering areas such as pollution, barometric pressure, and water level monitoring. Among these, rainfall monitoring is crucial for minimizing rainstorm damage and supporting agriculture by providing actionable insights. Traditional rainfall detection methods rely on physical devices to collect and calculate precipitation rates but often require frequent maintenance and calibration, limiting their reliability and scalability [1, 2, 3]. In recent years, leveraging ubiquitous communication links for rainfall monitoring has emerged as a promising research area. The variation in atmospheric conditions [4] can impact signal characteristics [5]. Raindrops, as a type of atmospheric particles, attenuate signal strength during signal propagation. The relationship between signal attenuation \(A\) (in dB) and rainfall is expressed as \(A=aR^{b}L\), where \(a\) and \(b\) are related to the signal frequency, polarization state, and the drop size distribution of the rain; \(R\) is the rainfall rate (in mm/h); and \(L\) is the path length (in km) [6]. The International Telecommunication Union provides standardized parameter values to calculate rainfall attenuation under different wireless configurations [7]. This attenuation effect allows base stations (BSs) to function as virtual sensors for rainfall monitoring [8]. Exploiting the interaction between wireless signals and environmental factors provides a cost-effectiveand sustainable approach, as it leverages existing infrastructure. The viability of commercial microwave links (CMLs) for extended, widespread operational rainfall monitoring purposes has been demonstrated [9]. Both model-based and data-driven methods for rainfall estimation using CML data have been comprehensively reviewed [10]. Furthermore, studies have highlighted the accuracy, algorithms, and results of precipitation monitoring based on CML signal levels [11; 12; 13]. However, acquiring CML data from BSs is often challenging. In many cases, access to such data requires collaboration with local network operators. Moreover, the widespread deployment of underground fiber-optic cables for inter-tower communication poses additional challenges for obtaining CML data, even for network operators [14]. In contrast, wireless communication links between user terminals and BSs are much more available nowadays. Key signal indicators, such as reference signal received power (RSRP), received signal strength indicator (RSSI), and reference signal received quality (RSRQ), are readily available on mobile devices, as defined by cellular standards. Compared with CML data, user terminal data can primarily provide measurements of the received signal. Research on leveraging the above signal data collected from user devices for weather sensing is significantly less explored. Investigations of the effect of rainfall on the above signal data collected by user terminals are summarized in Table 1. For instance, the daily average RSSI values of 2G links decrease during rainfall events [15], while another study observed that average RSSI values vary with rain intensity [16]. However, the predicted RSSI degradation during rain is not always evident in practice, with occasional instances showing no significant change [15]. An inverse relationship between rainfall and RSRP is observed under a line-of-sight (LoS) scenario in the 3.5 GHz citizens broadband radio service (CBRS) [17]. Most studies relying on smartphone-measured signals require locations with direct sight paths between BSs and data collection points to ensure LoS conditions, limiting their applicability for broader rainfall-sensing implementations.