# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 4 Discussion and Future Opportunities  
To support the design and optimization of MIMO antennas with SRR-based isolation enhancement, three distinct datasets were constructed and used to train predictive models. Database 1 focused on estimating the S11 response over the 20-34 GHz range by systematically varying key physical parameters, such as patch dimensions (Lp, Wp), substrate size (Ls, Ws), and transmission line parameters (Lf, Wf). This allowed the algorithm to capture the electromagnetic behavior with high accuracy, achieving a MSE of 0.1977 and a coefficient of determination R\({}^{2}\) = 0.9929. Database 2 was aimed at predicting the effects of SRR geometric variations on both S11 and S12. The input features included SRR design variables, such as ring radius (r1), the width of this ring (e1), the substrate's side length (gx), and the lengths of the split gap (xl), covering a wide frequency span (20-34 GHz). The model performance was robust, yielding R\({}^{2}\) = 0.9850, RMSE = 0.7429 dB, and MAE = 0.3080 dB for S11 and R\({}^{2}\) = 0.9880, RMSE = 0.8418 dB, and MAE = 0.4029 dB for S12. For Database 3, the objective was to optimize the SRR placement strategy for enhancing isolation at 26 GHz. Separate models were trained to predict S11 and S12, using the number of SRR cells (ns) and their vertical positions (yp) as inputs. Here, yp = 0 mm corresponds to the midpoint of the substrate, while symmetric placements were explored for the values of yp in both positive and negative directions. The model predicting S12 achieved R\({}^{2}\) = 0.9998, RMSE = 0.0924 dB, MAE = 0.0223 dB, and for S11, an outstanding R\({}^{2}\) = 0.9998, RMSE = 0.0284 dB, MAE = 0.0137 dB, indicating excellent generalization across the operating band. Each database consists of a large number of entries, which are divided into training and testing sets to evaluate the model performance. Specifically, 80% of the data are used for training the AI model, while the remaining 20% are reserved for testing. To analyze the results, the machine learning algorithm predicted that the optimal number of SRR cells was two, with the best position located at yp = 2.8 mm. As shown in Figure 25, the reflection coefficient S11 significantly improved after applying the isolation technique, achieving an enhancement of 9.15 dB at 26 GHz, while maintaining a wide bandwidth of 33.2%. Simultaneously, the transmission coefficient S12 exhibited a remarkable reduction, with an improvement of nearly 21 dB at the same frequency, confirming the effectiveness of the proposed SRR-based isolation approach. Isolation is quantitatively assessed via the S12 coefficient, which shows a nearly 20 dB improvement at 26 GHz with the two-cell SRR configuration. Additionally, the surface current distribution plots clearly visualize how the SRRs redirect and suppress coupling currents, further confirming the enhancement in isolation. Figure 26 presents the surface current distribution of the proposed MIMO antenna at 26 GHz, comparing the configurations without and with the addition of two SRRs, whose positions were selected by an AI-based optimization algorithm. The comparison was carried out by exciting only one of the antenna elements while the other remained inactive in order to observe the resulting surface current distribution and evaluate the level of mutual coupling. In Figure 26a, which represents the case without SRRs, intense surface current coupling is clearly visible between the two antenna elements, particularly in the region between the patches and microstrip lines, indicating strong mutual interactions and limited isolation. Conversely, Figure 26b shows the improved current distribution when two SRRs are introduced at strategically optimized locations. These positions were automatically determined by our ML algorithm. It demonstrates a clear reduction in surface current intensity in the same regions.