# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 4 Signal Pre-Processing  
In this section, we introduce the signal pre-processing methods employed in this work to better prepare the captured data for proceeding analysis.  
The first step is cell separation. As mentioned in Section 3.2, different cells can be recorded even when the mobile terminal is stationary. Therefore, it is necessary to group the captured data entries by their associated cells and select the primary cell to which the mobile terminal is most frequently connected. As shown in Table 3, the fields 'XCI' and 'LOCAL_CID' are used to identify and categorize the data entries under different cells.  
Figure 4: Radio propagation environment between cell and user terminal: (**a**) Cannot see the cell through visual inspection. (**b**) No LoS path was found in the radio propagation simulation. (**c**) The reflected path was found in the radio propagation simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline PLMN & XCI & xNBID & LOCAL\_CID & \multicolumn{2}{c}{LAC\_TAC} \\ \hline
50,502 & 20,781,625 & 81,178 & 57 & 52,010 \\ \hline BAND & RSSI & RSRP\_RSCP & RSRQ\_ECIO & SNR & LAT \\ \hline
2600 B7 & \(-67\) & \(-101\) & \(-13\) & \(13\) & \(-33.886251\) \\ \hline LON & BANDWIDTH & CA & NR\_STATE & DATE & TIME \\ \hline
151.195737 & 20,000 & 1 & none & 23 March 2024 & 10:02:56 \\ \hline
151.195737 & 20,000 & 1 & none & 23 March 2024 & 10:03:01 \\ \hline
151.195737 & 20,000 & 1 & none & 23 March 2024 & 10:03:06 \\ \hline \end{tabular}
\end{table}
Table 3: A snippet of the cellular signal measurements.  
From the data collected at the three locations described in Section 3, we can identify the main cell at each location. The results of this cell identification process are summarized in Table 4.  
For data entries associated with each cell, we begin by performing data cleansing, which includes removing duplicate entries and interpolating missing values to ensure that measurements are aligned to consistent time intervals. Typically, the start and end times of the exported signal data file correspond to the moments when the control button on the screen is pressed to initiate and terminate recording. However, there are instances where the record does not end properly after the stop button is pressed. Consequently, only the measurements from the most recent file are retained for analysis. Records containing 'nan' values for all signal measurements are excluded.  
Since mobile data and weather data have different temporal resolutions, time synchronization and alignment are necessary. The weather data resolution is limited to five-minute intervals, as provided by the open database used [32], which is significantly lower than the temporal resolution of the mobile data. To address this, interpolation is applied to match the timestamps of the two datasets. Specifically, the values from the nearest available timestamp are used to fill in any missing mobile data. The Modified Akima Cubic Hermite interpolation method [36] is then used to estimate the precipitation rate for each corresponding mobile data entry. This interpolation is implemented using the'makima' method of the 'interp1' function in MATLAB. Additionally, any interpolated precipitation values that result in negative values are set to zero, ensuring that the data remain valid.