# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 3 AI-Driven Optimization of MIMO Isolation Using SRRs  
In this part, we will create a MIMO antenna system with two elements, concentrating on improving its performance. The aim is to increase the separation between the two antenna parts, which will help in boosting signal quality and reducing interference. To do this, we will use split ring resonators (SRR) in our design, which will be fine-tuned with the help of machine learning. The AI will help in picking the most effective SRR settings to improve isolation and ensure good performance at the desired frequencies. By conducting simulations and assessments, we will look at how various SRR designs affect the overall effectiveness of the MIMO system, with the goal of achieving a strong isolation and efficient MIMO antenna operating at 26 GHz. Figure 12 illustrates the placement of two identical adjacent patch antennas separated by a short edge-to-edge distance of \(e=6\,\mathrm{mm}\), which is approximately equal to \(\lambda 0/2\) at 26 GHz. This close proximity leads to strong electromagnetic coupling between the radiating elements. As a result, the mutual coupling S12 level observed in Figure 13 reaches about \(-20\,\mathrm{dB}\), which is considered insufficient for MIMO applications. This high level of coupling negatively affects the S11 of each antenna element compared with the single antenna configuration, thereby degrading the overall performance in terms of impedance matching and isolation. To mitigate the high mutual coupling observed in the initial configuration, we propose the integration of SRRs structures as an isolation technique. The novelty of our approach lies in the coupling of this method with artificial intelligence (AI) to predict the optimal SRR dimensions and placement. The objective is to create a stop-band effect at 26 GHz, where the reflection coefficient S11 remains close to zero, while the transmission coefficient S21 is minimized (more negative), ensuring strong isolation between the antenna elements. Figure 11: Comparison of simulated and predicted S11 for the single antenna at 28 GHz. For that, we follow the same methodology as our previous AI-based model developed to predict the S11 performance of a single antenna based on its geometric dimensions. In the current study, we extend this concept by applying ML to predict the reflection and transmission behavior of SRR cells. In addition to this, a dedicated database was prepared to predict the SRR dimensions responsible for achieving a stop-band response, as previously reported in the literature. Figure 12: Structure of the proposed MIMO antenna array. Figure 13: Simulated S-parameters (S11 and S12) of the proposed MIMO antenna array. explained. Another database was then used to evaluate the influence of the number of SRR cells (periodic structures) and their positions along the Y-axis between the MIMO antenna elements while keeping the X-axis position fixed to preserve symmetry. The proposed AI model is designed to predict both the S11 and S21 parameters of the MIMO antenna system by finding a compromise between maintaining good impedance matching and achieving optimal isolation. As a first step, we prepared a dedicated database containing various SRR geometries, their number (periodic cells), and positions along the Y-axis between MIMO antenna elements over the 20-34 GHz frequency range. This database was built to capture the impact of these parameters on the antenna's reflection (S11) and transmission (S21) responses. The dataset was then preprocessed to ensure consistency and completeness, followed by normalization of all input features for improved compatibility with machine learning algorithms. To accurately predict and optimize MIMO isolation performance, we trained this dataset using the random forest algorithm. This model approach enabled us to benchmark the performance of this algorithm and identify the predicting S11 and S21 coefficients, taking into account the trade-off between good impedance matching and strong isolation.