# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 3 AI-Driven Optimization of MIMO Isolation Using SRRs
### AI Model 2 for SRR Dimension Prediction  
In Figure 14, the geometry of the SRR is illustrated with all relevant design parameters. The geometry of this cell used for isolation enhancement in the MIMO antenna system is defined by several critical design parameters. The substrate's side length is denoted by gx, which determines the overall footprint and positioning of the SRR structure relative to the antenna elements. The outer side length of the metallic resonator ring is represented by r1, which significantly influences the resonant behavior of the SRR. The metallic trace width of the ring is labeled as e1, and the spacing between the inner and outer ring segments is defined as g = 0.4 mm. The open gap in the resonator, essential for its capacitive behavior, is described by xl, corresponding to the vertical lengths of the split gap. These parameters play a critical role in determining the resonant frequency and stop-band behavior of the SRR. Figure 14: Geometric illustration and labeled parameters of the split ring resonator (SRR) unit cell used for mutual coupling reduction. To predict the electromagnetic behavior of the MIMO antenna system incorporating SRR structures, a supervised learning model based on the random forest regressor was developed. The algorithm was trained on a comprehensive dataset containing geometric parameters (r1, gx, e1, and xl) of the SRR and the resulting S-parameters (S11 and S12) over a broad frequency range (20-34 GHz). The goal was to accurately estimate the reflection and isolation characteristics, especially around the target frequency of 26 GHz. Additionally, the training time remained reasonable--even for a dataset containing 16,100 entries, training the random forest model required less than one hour on a standard personal computer. This high efficiency, combined with strong predictive power, justifies our choice to apply the random forest model to all datasets in this study. Furthermore, the modular nature of our framework makes it scalable to more complex MIMO topologies and isolation techniques in future implementations. This is summarized in Figure 15, which illustrates the complete AI-driven workflow for SRR optimization. The model's effectiveness was assessed through metrics from random forest regression. When predicting S11, the model recorded an R\({}^{2}\) score of 0.9850, with a root-mean-square error (RMSE) of 0.7429 dB and a MAE of 0.3080 dB. The results for S12 were also impressive, showing an R\({}^{2}\) score of 0.9880, an RMSE of 0.8418 dB, and an MAE of 0.4029 dB. With this trained model, an ideal SRR setup was forecasted to reduce mutual coupling (S12) at 26 GHz. The suggested configuration achieved a notably low S12 measurement of \(-54.84\) dB, demonstrating outstanding separation between the two MIMO components. The corresponding reflection coefficient S11 was also acceptable at \(-0.54\) dB, indicating a manageable mismatch at the input port. The optimized SRR design parameters that contributed to this outcome included: e1 = 0.5 mm, gx = 5.5 mm, r1 = 4.8 mm, and xl = 0.7 mm. A visual overview of these findings can be found in Figure 16 (screen captures). These figures can now act as a guide for practical application and additional verification. A comparison between the predicted and simulated values of the S-parameters further validates the accuracy of the proposed random forest-based model. As illustrated in Figure 17, the predicted and simulated curves of S11 exhibit a high level of agreement across the frequency band of interest, particularly around 26 GHz, where the optimization is targeted. Similarly, Figure 18 presents the results for S12, where the predicted isolation closely follows the simulated values, with negligible deviation. The nearly identical trends  
Figure 15: Machine learning (ML) workflow illustrating the process of optimizing split ring resonator (SRR) parameters for improved antenna isolation.