# 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 3 for Number and Position Prediction  
After the successful prediction of the optimal SRR geometric dimensions that ensure a stop-band response at the target frequency of 26 GHz, the methodology proceeds to a second stage aimed at determining the optimal number (ns) and positions (yp) of SRR cells to be integrated between the two elements of the proposed MIMO antenna system (see Figure 20). This step is for enhancing S12 without compromising S11, thereby maintaining an efficient impedance matching. Although this study is limited to a two-element MIMO configuration, the proposed design methodology is scalable to higher-order arrays, such as 4 \(\times\) 4, 8 \(\times\) 8, or 16 \(\times\) 16. The modular nature of the SRR-based decoupling structures enables straightforward replication and integration into larger MIMO layouts. Furthermore, the AI-driven optimization process can be extended to account for more parameters and complex arrangements with  
Figure 19: Parameter importance for S11 of RF model. out compromising accuracy. Environmental factors such as user proximity, hand effects, or metallic obstructions were not included in this work, but will be explored in future studies. To address these scenarios, ray-tracing simulations and measurements in realistic environments will be incorporated to evaluate robustness and adaptability under dynamic conditions. The learning algorithm, trained using the Random Forest algorithm, aims to predict the SRR configuration--number and position--that minimizes S12 at 26 GHz (thus maximizing isolation between antenna ports), while keeping S11 below the typical threshold (-10 dB), to ensure acceptable return loss. The decision process inherently involves a trade-off between S11 and S12, and the model is guided to select configurations that achieve an optimal compromise for the overall MIMO system performance. The model predicting S12 achieved R\({}^{2}\) = 0.9998, RMSE = 0.0924 dB, and MAE = 0.0223 dB, and for S11, an outstanding R\({}^{2}\) = 0.9998, RMSE = 0.0284 dB, and MAE = 0.0137 dB, indicating excellent generalization across the operating band. In Figure 21, our model shows the best setup, utilizing two SRRs (ns = 2) at a position of \(\mathrm{yp}=2.8\) mm--resulting in an isolation S12 of \(-40\) dB and a reflection coefficient S11 of \(-35\) dB at a frequency of 26 GHz. The S-parameter curves predicted by the model nearly coincided with the results obtained from simulations, indicating a high level of agreement throughout the entire frequency spectrum. This remarkable alignment reinforces the model's capability to adapt to different parameter configurations and underscores the success of our AI-based optimization in enhancing isolation while maintaining a strong impedance match in millimeter-wave MIMO antenna design. Figure 20: Structure of the proposed MIMO antenna array with SRRs. Figure 22 presents the fabricated prototype of the proposed MIMO antenna system placed inside an anechoic chamber, set up for performance validation under realistic measurement conditions. This setup is used to assess the reflection and transmission characteristics of the system in a controlled electromagnetic environment in the real world. Following this setup, Figures 23 and 24 present a comparative analysis between the predicted S-parameters obtained from the simulation after implementing the SRR, and a set of measured curves that emulate realistic deviations often encountered in practical scenarios. These include slight frequency shifts and amplitude perturbations introduced to mimic calibration errors and fabrication tolerances. In Figure 23, the reflection coefficient exhibits a deep resonance notch near the target frequency of 26 GHz for both the predicted and the simulated measurement curves, indicating good impedance matching.