# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 4 Discussion and Future Opportunities
Figure 28: Radiation pattern of single-antenna MIMO array without and with isolation for: (**a**) Phi = 0\({}^{\circ}\), (**b**) Phi = 90\({}^{\circ}\). Figure 29: ECC and DG of the proposed MIMO antenna array with SRRs. It approaches the optimal value of 10 dB, confirming the system's high diversity performance under multipath conditions. The total active reflection coefficient provides insight into the total reflection when both ports are simultaneously excited and is computed as:  
\[TARC=\frac{1}{\sqrt{2}}\sqrt{\left.\right|S11+S12\left.\right|^{2}+\left.\right| S22+S21\left.\right|^{2}} \tag{7}\]  
At 26 GHz, TARC is less than \(-30\) dB (or \(<\)0.1 in linear scale), signifying excellent impedance matching and minimal active reflection under multi-port excitation. Finally, the channel capacity loss (CCL), derived from Equation (8), quantifies the reduction in theoretical MIMO channel capacity due to mutual coupling and mismatch between elements. It is calculated using the following expression:  
\[CCL=-\sum_{i=1}^{N}log2(\lambda i) \tag{8}\]  
where \(\lambda i\) represents the eigenvalues of the matrix \(I-SH\), \(I\) is the identity matrix, and \(S\) is the scattering matrix composed of the S-parameters (S11, S12, S21, S22). This formulation allows for the direct evaluation of the impact of mutual coupling on channel performance based on simulated S-parameters. The low CCL values obtained near 26 GHz confirm minimal capacity degradation, reinforcing the suitability of the proposed antenna for high-data-rate 5G mmWave MIMO applications. Table 4 provides a detailed comparison of the related work, highlighting the potential and advancements of our proposed approach. It demonstrates how our work outperforms or addresses key limitations observed in previous studies, showcasing the unique contributions and potential of our method in advancing the field. Table 5 presents a comparative analysis of recent works employing machine learning (ML) techniques for antenna design and performance prediction. The comparison includes key parameters such as operating frequency range, number of elements, ML model type (e.g., Random Forest, KNN, KBNN), achieved gain and isolation, structural complexity, and the accuracy of the prediction model using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R\({}^{2}\)). The proposed design in this work, based on a Random Forest model at 26 GHz with two elements, demonstrates  
Figure 30: TARC and CCL of the proposed MIMO antenna array with SRRs. superior prediction accuracy (MSE = 0.02, R\({}^{2}\) = 0.99), high isolation (21 dB), and a relatively simple structure compared to previous complex designs in the literature. The integration of intelligent optimization techniques in MIMO antenna design opens up several promising research directions. Building upon the current work, which employs machine learning to enhance gain and reduce mutual coupling at 26 GHz, future research can explore advanced AI models, such as deep learning and reinforcement learning, to predict and optimize complex antenna geometries and electromagnetic behaviors with even higher accuracy and generalizability. In particular, the approach of ML-guided isolation enhancement using SRR structures, as demonstrated in this work, can be extended to adaptive and reconfigurable MIMO systems using tunable metamaterials or intelligent control circuits, enabling real-time performance optimization in dynamic 5G and emerging 6G environments. This is especially relevant for edge-connected IoT and vehicular networks, where robust beam control and interference mitigation are critical. Moreover, the exploration of on-chip MIMO arrays and the use of novel flexible or biocompatible materials offer potential applications in wearable or implantable biomedical systems.