# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis
### Variations in Measurements During Dry Period
#### 5.1.1 Small-Scale Signal Features in Dry Days  
The minimum sample interval is one second in the mobile application. A one-hour continuous time window is selected to inspect the small-scale signal variations, as shown in Figure 5.  
Figure 5 shows significant fluctuations in signal measurements throughout the duration of an hour. We see consistent signal variations on the second level, which is less  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Main Cell** \\ **ID** \\ \end{tabular} & \begin{tabular}{c} **Sample** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Band** \\ **(MHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Bandwidth** \\ **(MHz)** \\ \end{tabular} &
\begin{tabular}{c} **Distance to** \\ **User** \\ **Terminal (m)** \\ \end{tabular} \\ \hline
1 & 20781625 & 1,339,744 & 2600 MHz B7 & 20 & 195.83 \\
2 & 20781625 & 751,792 & 2600 MHz B7 & 20 & 181.08 \\
3 & 135148290 & 9450 & 1800 MHz B3 & 15 & 392.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main cell information for each dataset.  
likely to be caused by environmental factors to be sensed. Such variations hence need to be suppressed or mitigated for accurate environmental sensing.  
Environmental factors, like temperature and humidity, might influence signal measurements. To investigate this, Dataset2, collected outdoors, is selected for analysis. At the same time as mobile signal metrics are collected, temperature and humidity measurements collected outdoors are recorded. Specifically, the time period from 1 a.m. to 2 a.m. is chosen due to less human activity near the data collection devices. Figure 6 presents one hour of signal measurement samples alongside matched temperature and relative humidity data during dry conditions. The signal measurements fluctuate much more frequently than the changes in temperature and relative humidity. Temperature only varies by 0.3 degrees while remaining constant in relative humidity. Notably, signal measurements continue to vary even when temperature and humidity remain stable from 1:20 a.m. to 1:40 a.m. This is consistent with the indoor experiment, where fluctuations in the RSSI occur without interference or disturbance [37].  
Figure 5: One\(-\)hour signal measurements in Dataset1.  
Figure 6: Signal measurements within an hour during the dry period in Dataset2.  
Figure 7 presents examples of signal samples from a one-hour dry period for the other two datasets. It is evident that fluctuations in RSSI measurements are more than those in RSRP measurements in Dataset1, as shown in Figure 7a, which aligns with findings from a previous study [17]. Additionally, Dataset3 exhibits fewer oscillations in Figure 7b, likely due to its larger sampling interval compared to the other two datasets.  
The results illustrated above indicate that small-scale signal fluctuations exist even in dry conditions. Such fluctuations should be suppressed or mitigated to avoid interfering with rainfall sensing. Processing methods that mitigate non-weather-related variations, such as averaging measurements within a specified time window [16; 23] and employing moving averages [25], have been utilized in previous studies. The moving average typically employs a time window of several minutes, using overlapping samples to compute averaged signal parameters from neighboring data points. In contrast, the averaging method utilizes non-overlapping time windows, usually with a duration of one hour. In this work, the average method among non-overlapping time windows has been employed.