# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 2 Model 1 for Antenna Dimension Prediction  
In this work, we deliberately adopt a simple rectangular patch antenna structure without introducing additional slots, stubs, or fractal geometries. This choice is motivated by a design focused on clarity, manufacturability, and efficiency. While complex shapes may offer certain advantages in specific contexts, they often introduce unnecessary complications when the application does not demand wideband operation or band-rejection features. Our results demonstrate that a carefully optimized basic geometry can deliver excellent performance in terms of return loss, gain, and radiation characteristics. We emphasize that complexity should not be introduced for its own sake, but rather when it directly contributes to the desired specifications. This approach not only facilitates better physical understanding, but also ensures easier integration into practical systems. To design the antenna, we employed classical equations based on transmission line theory to determine the optimal dimensions for a rectangular microstrip patch antenna operating at the n258 band (24.25-27.5 GHz). The design was implemented on an FR4 substrate with a dielectric constant of \(\xi r=4.4\) and a thickness of h = 1.6 mm. The free-space wavelength at 26 GHz was \(\lambda 0=11.54\) mm. Using the standard formulation, the physical width of the radiating patch was computed as [30]:  
\[\text{Wp}=\frac{\lambda 0}{2}\sqrt{\frac{2}{\xi r+1}}\ \approx\ 2.75\text{ mm} \tag{1}\]  
where:  
\[\xi eff=\frac{\xi r+1}{2}+\frac{\xi r-1}{2}\left(1+\frac{10h}{Wp}\right)^{ \frac{-1}{2}}\ \approx\ 3.2\text{ mm} \tag{2}\]  
The patch length is calculated by this equation:  
\[\text{Lp}=\frac{c}{2f\sqrt{\xi eff}}-2\Delta L \tag{3}\]  
where  
\[\Delta L=0.412h\frac{(\xi eff\ +\ 0.3)\left(\frac{w}{h}\ +\ 0.262\right)}{( \xi eff-0.258)\ \left(\frac{w}{h}+0.813\right)}\ \approx\ 0.61\text{ mm} \tag{4}\]  
\[\text{Lp}=2\text{ mm}\]  
Although the initial design based on classical analytical equations provides a functional antenna, the performance remains suboptimal. As illustrated in Figure 1, the S11 curve shows resonance at 26 GHz with a return loss of \(-12\) dB, which is relatively acceptable but does not indicate a strong impedance match. Furthermore, as shown in Figure 2, the antenna gain remains limited to around 4.5 dBi, which is considered low for millimeter-wave 5G applications that require higher gain. These limitations motivated the need to improve the antenna's performance by optimizing its physical dimensions. However, performing a conventional parametric sweep, where one parameter is varied while others are kept constant, can be extremely time-consuming and may not always lead to the optimal result. Moreover, the high sensitivity of mmWave antennas to geometric variations complicates this process. To address these challenges, we propose an AI-based modeling approach that allows us to rapidly and efficiently predict the antenna dimensions that yield optimal S11 characteristics. Figure 1: Simulated S11 parameter of the antenna with dimensions determined by the analytical design method. Figure 2: (**a**) Data acquisition workflow. (**b**) Flowchart showing the processes involved in creating machine learning. Figure 2 illustrates the overall process used in this study. In Figure 2a, we present the data acquisition workflow, which begins with the parametric simulation of antenna structures using the electromagnetic software (HFSS.13). Key parameters, such as patch dimensions (Lp, Wp) and transmission line (Lf, Wf) are varied systematically to generate a wide dataset of S11 responses over the 20-34 GHz frequency range. This step ensures a rich and diverse database covering a broad design space. The analytical method guides the selection of parameter ranges to be explored in the dataset, ensuring efficient and focused variation. After preparing and structuring this dataset, we proceed to the steps shown in Figure 2b, which outlines the flowchart of the machine learning process.