# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis
### Rainfall Impact on Signal Measurements
#### 5.2.1 Standard Deviation for the Different Weather  
To investigate signal features on rainy days, we begin by analyzing the SDs of RSSI, RSRQ, and SNR. However, this requires aligning the captured data based on time.  
Although the sample interval can be set manually to a fixed number, the actual sample interval may differ from this setting. This non-uniform sampling must be addressed before calculating the SD. There are two primary reasons for the variation in sample intervals. First, non-uniform sampling may arise from the device or application control. In this case, the phone maintains a connection with the same cell, but the sample interval deviates from the manual setting. Second, the switching of connected cells can interrupt data recording from the main serving cell. However, this non-uniformity constitutes a small portion of the overall dataset. For example, in Dataset1, over 94.37% of the sample intervals match the manual setting after selecting the signal data from the main serving cell. Of the remaining intervals, 5.48% of interval values deviate from the manual setting due to device control issues, while only 0.15% of deviations correspond to cell switching, resulting in intervals longer than the set value. Notably, more than 99% of the longer intervals caused by device control issues are less than one minute, and approximately 98% of the intervals resulting from cell switching are also less than one minute. In total, over 99.9% intervals are less than one minute.  
To address the non-uniform sampling, we first calculate the average measurement for one-minute intervals, which also helps mitigate fluctuations at the seconds level. Then, we apply linear interpolation to estimate any missing values within these one-minute intervals, provided the missing duration is less than two minutes. Next, we compute the SD over a five-minute time window using these one-minute average measurements. If any missing values occur within the time window, that window is excluded from the SD calculation. The SD values are further categorized into dry and rainy classes based on the mean precipitation rate. Specifically, when the mean precipitation rate equals zero, the data are labeled as dry; otherwise, they are labeled as rainy. The SD histograms, along with the Probability Density Function (PDF) and the normal fit of the SD for dry and rainy periods during rainy days across different datasets, are presented in Figure 10.  
From Figure 10, we can see that there is no significant difference in signal SD between rainy time and dry time. The distribution of the normal fit for different signal metrics under different weather conditions looks quite similar. The signal SDs during rainy periods are not larger than those during dry periods, similar to findings from an indoor experimental study [38]. Even during dry periods, there are notable fluctuations in signal measurements within an hour, as discussed in Section 5.1.1. The PDF distributions for the SD in rainy and dry conditions are almost the same across all datasets. This observation suggeststhe limitations of using SD alone to assess the impact of rainfall on signal measurements, especially in complex microwave propagation environments like indoor locations.  
Figure 10: SD statistic for signal measurements: (**a**) Histograms and PDFs for the SD for 13–14 December in Dataset1. (**b**) Histograms and PDFs for the SD for 4–6 April in Dataset2. (**c**) Histograms and PDFs for the SD for 4–6 April in Dataset3.