# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 6 Discussion  
This section provides an overview of the relevant studies and previous work. Potential future research directions will also be discussed.  
Rainfall would cause additional attenuation for radio signal propagation. Many papers studying signal power from CML have observed that the decrease in the signal attenuation and the increase in the precipitation rate matched very well with each other [39; 40]. The stability of the measured signal level appears to be influenced by weather conditions, particularly in the context of the 3G LoS scenario [19]. In studies that have collected LTE signal data under the LoS scenario, a discernible difference in the average received signal levels has been noted between rainy and non-rainy conditions [16]. Notably, a significant drop in RSSI has been observed since the beginning of rain [25]. This highlights the sensitivity of signal strength measurements to changes in weather, especially in scenarios where the signal propagation path is not obstructed. However, in our study, where data were collected under the NLoS scenario, such changes in signal measurements associated with rainfall conditions are not apparent. This discrepancy could be attributed to the complexity introduced by obstructions and reflections in the NLoS environment. The presence of multiple signal paths in NLoS conditions could potentially average out the effects of rain, leading to less noticeable fluctuations in signal strength compared to the more direct LoS scenario. Thus, we start with investigating the signal measurements during dry time. A daily signal fluctuation pattern has been observed in this work. Such no-rain-related variations make it challenging to distinguish the signal attenuation increases from rain or the daily pattern itself from the time domain.  
One of the biggest differences between CML data and data collected by the smartphone is their propagation environments, which makes the attenuation calculation difficult. Some studies adopt signal measurement during the dry time or previous period as the baseline and normalized the rain signal [16; 25]. Such process techniques assume that the transmit power does not change or the received power is not affected by other factors except rainfall over time. In this paper, data collection is conducted in real-life situations, and a daily pattern for the variation of the measured signal strength is found. This makes adopting previous dry time signal measurements as the baseline for calculating the attenuation unsuitable. Thus, we employed the spectral analysis method and found the increased power at lower frequencies in rainy days compared to dry periods.  
The diversity of the signal data could provide more information about the influence of rain. For example, CML data usually consist of many links with different frequencies, link distances, and polarization directions. These factors all impact the signal measurement [15]. A rainfall influence on the attenuation when transmitter and receiver are both indoors with NLoS path in self-built 2 GHz horizontal polarization microwave link is studied [38]. Further developed applications with higher sample frequency might speed up the research. Except for the atmospheric effects, network load might also impact the received signal metric. Separation of such an impact would be a direction for future work.  
Most existing works are conducted under the GSM/LTE network, which has been gradually replaced by the 5G network. From the open literature, no rainfall prediction study has been conducted based on signal data collected by a user terminal under a commercial 5G network. The possibilities of utilizing sub-6 GHz signals with NLoS links as a rainfall monitoring method have been reviewed [41]. By investigating the impact of rainfall on 5G network signal measurements, 5G base stations can be potentially repurposed as opportunistic virtual rain gauges.