# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction
### Related Work  
In [19], the authors propose a machine learning-based approach for real-time tuning of the impedance matching network in a slotted meandered patch antenna operating within the 2-3 GHz band. A regression model, based on the Random Forest algorithm, was developed to predict key circuit parameters, such as inductance, capacitance, and the reflection coefficient (S11), using the antenna's slot dimensions and operating frequency as input features. The model was trained on a dataset of 500 samples generated via electromagnetic simulations, with target values derived from an equivalent RLC circuit representation. The performance of the model was validated using 10-fold cross-validation, yielding a high coefficient of determination (R\({}^{2}\) = 0.98) and a maximum prediction error of 12.46% for S11. Compared with conventional vector network analyzer (VNA)-based tuning methods, this ML-driven approach significantly reduces computation time and demonstrates strong potential for real-time reconfigurable and adaptive antenna systems. However, the relatively high maximum error in S11 prediction may limit its applicability in high-precision or mission-critical scenarios, where tighter error bounds are required. In a related study [20], a compact L-band microstrip patch antenna with integrated meander lines and symmetric slots was designed to achieve substantial miniaturization while enhancing key performance metrics. Starting from a conventional rectangular patch antenna with a resonant frequency of 1.38 GHz, a gain of 2.23 dB, and a bandwidth of 20 MHz, the proposed miniaturized design decreased the antenna volume by 92%--from 150 \(\times\) 150 \(\times\) 1.6 mm\({}^{3}\) to 40 \(\times\) 45 \(\times\) 1.6 mm\({}^{3}\)--while delivering a 300% increase in bandwidth and a 124% improvement in gain without altering the resonant frequency. The study also explored the application of four machine learning algorithms--regression trees, support vector regression (SVR), kernel least squares, and artificial neural networks (ANNs)--to optimize the antenna parameters using Bayesian optimization. The results demonstrated the capability of ML models to reduce computational errors and yield accurate design predictions. Nonetheless, the implementation of multiple ML techniques introduces increased complexity and computational overhead, which could pose challenges for large-scale or real-time design environments. In [21], the authors present a machine-learning-assisted methodology for the design and optimization of a compact MIMO antenna tailored to future 6G applications in the terahertz (THz) band. The antenna, implemented on a polyimide substrate with compact dimensions of 0.614\(\lambda_{0}\)\(\times\) 0.614\(\lambda_{0}\), demonstrates outstanding performance, including a peak gain of 15 dB, an isolation level exceeding 31 dB, and a high radiation efficiency of 97.40%. Operating over a wide bandwidth of 85.71%, the antenna covers both sub-THz and THz frequency ranges while maintaining a low envelope correlation coefficient (ECC \(<\) 0.0012) and high diversity gain. Five machine learning models were compared for predicting performance metrics, with random forest regression (RFR) outperforming the others in terms of accuracy (R\({}^{2}\) \(>\) 0.95) and error minimization, establishing it as the most reliable method for gain prediction. The integration of ML significantly reduced the computational cost associated with traditional full-wave simulations. Nevertheless, a key limitation is the exclusive reliance on synthetic data from CST simulations for training, which may hinder the model's generalizability when accounting for fabrication imperfections and environmental variability. In [22], a four-port MIMO antenna intended for 5.9 GHz vehicle-to-everything (V2X) communications is proposed. The design employs a tapered radiating structure combined with a defected ground plane and a stepped impedance feedline, fabricated on an FR4 substrate with compact dimensions of 96 \(\times\) 64 \(\times\) 0.8 mm\({}^{3}\).