# A REM Update Methodology Based on Clustering and Random Forest
## 4 Evaluation
### REM Update Evaluation
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Time (s)**} \\ \hline
**Model for Prediction** & **Historical Data \(D_{H}\)** & **Data at \(\mathbf{t}_{1}\)** & **Data at \(\mathbf{t}_{2}\)** & **Data at \(\mathbf{t}_{3}\)** \\ \hline RF 4 clusters & 0.539 & 0.366 & 0.375 & 0.418 \\ RF without clusters & 0.213 & 0.215 & 0.234 & 0.341 \\ SVR 4 clusters & 0.111 & 0.205 & 0.231 & 0.374 \\ MLP 4 clusters & 1.14 & 0.669 & 1.48 & 1.13 \\ AdaBoost 4 clusters & 0.502 & 0.234 & 0.369 & 0.301 \\ \hline \hline \multicolumn{4}{c}{**Grid Prediction Time (s)**} \\ \hline
**Model for Prediction** & **100 \(\times\) 100 grid** & **300 \(\times\) 300 grid** \\ \hline RF 4 clusters & 0.112 & 0.285 \\ RF without clusters & 0.04 & 0.251 \\ SVR 4 clusters & 0.195 & 1.25 \\ MLP 4 clusters & 0.038 & 0.218 \\ AdaBoost 4 clusters & 0.134 & 0.625 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational time for the training and prediction phases. Figure 9: Target REM after the relocation of the AP by using RF. we can see that only the left area of the room was updated to the actual wireless coverage condition because the newly collected data at time \(\mathbf{t}_{1}\) do not have information about the remaining areas of the room. Next, by using the newly collected data at time \(\mathbf{t}_{2}\), the upper area of the room was updated, while the bottom right area remained unaltered because of insufficient measurements. Finally, the entire room was updated with the newly collected data at time \(\mathbf{t}_{3}\), as evidenced by comparing it to Figure 9.  
Table 4 presents the error evaluation for the newly collected data after the relocation of the AP at three different times by using the RF algorithm. Similar to the results obtained in Table 2, the error in the prediction decreased as time passed because new clusters could be updated as new measurement data were collected. For instance, the low error observed at time \(\mathbf{t}_{3}\) is in concordance with the REM presented in Figure 11c, which is very similar to the ideal REM of Figure 9. In the case of the baseline scheme without clustering, the error remains consistent over time, and the errors at each time are greatly influenced by the location of the newly collected data. Figure 11: REMs obtained with the proposed update mechanism after the relocation of the AP. (**a**) The REM at time \(\mathbf{t}_{1}\); (**b**) The REM at time \(\mathbf{t}_{2}\); (**c**) The REM at time \(\mathbf{t}_{3}\). Figure 10: The three areas considered for newly collected measurements after the relocation of the AP. (**a**) Data collected at time \(\mathbf{t}_{1}\); (**b**) Data collected at time \(\mathbf{t}_{2}\); (**c**) Data collected at time \(\mathbf{t}_{3}\). Recently, reconfigurable intelligent surfaces (RIS) have been explored as a potential solution to enable a smart radio environment that can be dynamically configured using software. RIS is a type of metasurface that can be used to manipulate radio waves in order to control wireless communication [24]. An RIS is essentially a two-dimensional array of small, controllable elements that can adjust the phase and amplitude of incident electromagnetic waves to create a desired wavefront. Using RIS can enable the creation of smart environments that can adapt to changing electromagnetic conditions in real time. Field-programmable gate array can be used to control the operation of the RIS, or it can be manually controlled, as proposed in [25], by using touch controls. Therefore, analyzing the information in the REM can enable the optimization of the placement and reflection properties of an RIS to maximize the performance of wireless communication systems. Moreover, the REM can facilitate the dynamic adjustment of the reflection coefficients of RIS elements to adapt to changes in the wireless environment. For example, a REM can be used to identify areas with poor wireless coverage, such as areas with high levels of interference or signal attenuation.