# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 2 Model 1 for Antenna Dimension Prediction
Boosting this number typically enhances prediction consistency and lowers variance, since more trees can identify varied patterns within the data. On the other hand, the depth indicates the highest number of splits each tree is allowed; a deeper tree can represent more complicated relationships, but may lead to overfitting if it is excessively deep. It is essential to note that, past a certain point, further increasing these parameters, especially the number of estimators, yields lesser improvements in prediction accuracy while greatly delaying the training and inference stages. So, we adopted the setup with 400 trees and a depth of 30 produced the most precise predictions (S11 = \(-\)45 dB), especially at 26 GHz (see Figure 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Ls** & **Ws** & **Lp** & **Wp** & **Lf** & **Wf** \\ \hline Value (mm) & 11.50 & 11.50 & 2.95 & 6.20 & 5.54 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensions of the proposed antenna for the 26 GHz band. Figure 3: Structure of the proposed antenna with patch dimensions optimized by machine learning prediction. This suggests that raising both the number of trees and the depth gave the model enough ability to understand the nonlinear connection between the antenna's dimensions, the frequency, and the resulting S11 outcome. Consequently, these two hyperparameters are vital for enhancing the learning process, and accurately adjusting them can result in highly dependable predictions, as shown by the close match with the full-wave simulation. The proposed algorithm demonstrates a high level of precision in predicting the S11 parameter (\(-45\) dB) of the antenna structure at 26 GHz and achieves a wide operational bandwidth from 24.4 GHz to 31.2 GHz, providing a 6.8 GHz bandwidth that spans approximately 26.15% of the central frequency. Based on the optimized dimensions extracted from the model, the random forest-based regression model successfully estimated the reflection coefficient across the frequency band from 20 to 34 GHz. As illustrated in Figure 5, the predicted S11 curve closely follows the trend of the simulated data, with only a minimal deviation of approximately 0.5 dB at 26 GHz. This slight offset is considered negligible in practical antenna design, confirming the robustness of the learning algorithm in capturing the underlying electromagnetic behavior. The consistency between the predicted and simulated values strongly validates the model's ability to generalize and optimize the antenna parameters without requiring time-intensive full-wave simulations for each configuration. Unlike traditional analytical or empirical approaches that depend on simplified models and idealized assumptions, AI-driven predictions provide a notable advantage ein performance. In particular, our algorithm demonstrates an enhancement of up to 34 dB in S11 at the desired resonance frequency when compared with standard analytical forecasts (see Figure 6). This improvement is mainly due to the algorithm's ability to analyze complicated nonlinear connections between geometry and antenna performance over a broad range of frequencies. Conventional analytical methods may struggle to adequately represent detailed electromagnetic interactions, particularly in the millimeter-wave range, where factors like substrate effects, coupling, and higher-order modes are significant. In contrast, the AI model adjusts to these changes by learning from accurate simulation data. The result is a forecasting tool that not only improves matching at the target frequency, but also speeds up the design process by minimizing the number of trial-and-error simulations usually needed in standard methodologies. Figure 4: S11 variation with changes in random forest model parameters. At 26 GHz, the proposed antenna gain reaches over 6.7 dBi, as shown in Figure 7, highlighting a notable gain improvement of approximately +2 dBi, rising from 4.56 dB to 6.64 dB, which further validates the effectiveness of our AI-based optimization methodology.