# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction  
The rapid evolution of 5G and beyond-5G communication systems has intensified the demand for high-performance multiple-input multiple-output (MIMO) antennas capable of operating at millimeter-wave (mmWave) frequencies, particularly in the 26 GHz band [1]. MIMO technology enhances channel capacity and spectral efficiency by exploiting spatialmultiplexing [2]. However, a critical challenge in compact MIMO antenna design is mitigating mutual coupling between closely spaced radiating elements, which degrades system performance by reducing gain and increasing interference [3; 4; 5]. Several methods have been proposed to mitigate mutual coupling, such as the use of electromagnetic bandgap (EBG) structures, defected ground structures (DGSs), parasitic elements, neutralization lines, and metamaterial-inspired structures [6; 7; 8; 9]. Among these, split ring resonators (SRRs) have proven to be particularly effective due to their compact size, ability to introduce stop-band characteristics, and tunable resonance behavior [10; 11; 12]. Traditionally, the design and tuning of the antenna or SRR dimensions for optimal performances require extensive full-wave simulations and manual parameter sweeps, which are time-consuming and computationally expensive. Recently, machine learning (ML) techniques have emerged as powerful tools to accelerate the antenna design process by learning the complex relationships between geometric parameters and antenna performance from precomputed data [13; 14; 15; 16]. Numerous studies have explored the use of machine learning in the field of antenna design. It is believed that machine learning can speed up the antenna design procedure while ensuring a high degree of accuracy [17]. This includes reducing errors and saving time, predicting how the antenna will perform, improving computational efficiency, and cutting down on the number of simulations required. Generally, to implement machine learning in antenna design, the following procedures can be undertaken [18]:  
1. Through various simulations, the electromagnetic properties of an antenna are determined;
2. These properties are then recorded in a database and serve as a dataset for training a specific machine learning model;
3. After making predictions based on the designer's requirements, the algorithm creates a design for the antenna that yields the most accurate results. Various ML algorithms are compared in this work, including:  
* Linear Regression (LR): A simple, fast model that captures linear relationships between input features and performance metrics. However, it struggles with the nonlinear and complex dependencies typical in antenna designs;
* Decision Trees (DT): These models are interpretable and can handle nonlinearities, but they tend to overfit small datasets and may lack generalization;
* Gradient Boosting is an ensemble technique that builds models sequentially. Each new model attempts to minimize the error (residual) of the previous models by fitting a new decision tree to the gradient of the loss function. It is powerful for regression and classification tasks;
* SVR (support vector regression): The regression counterpart of the support vector machine (SVM). It tries to find a function that deviates from the actual observed targets by a value no greater than a certain threshold (epsilon), and at the same time is as flat as possible. It is useful for capturing complex relationships in data;
* Random Forest: An ensemble learning method that creates multiple decision trees and combines their outputs (by averaging for regression or voting for classification). It introduces randomness by using different subsets of features and data samples for each tree, leading to better generalization and reduced overfitting;
* ANNs (artificial neural networks): ANNs are computing systems inspired by the structure and function of the human brain. They consist of layers of interconnected \"neurons\" that process data. ANNs are widely used in pattern recognition, classification, regression, and deep learning tasks.