# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction
### Related Work
The antenna achieves an impedance bandwidth of 5.66-6.00 GHz, a peak gain of 7.85 dB, and an excellent radiation efficiency of 99%. To accelerate the design process and improve accuracy, multiple machine learning algorithms--including XGBoost, random forest, and deep neural networks--were employed within a stacking ensemble framework. This approach enhanced predictive accuracy and reduced the simulation workload. Key MIMO performance indicators (ECC, diversity gain, channel capacity loss, and TARC) were also assessed, confirming robust diversity performance. Moreover, the fabricated prototype exhibited close agreement with simulated results. A noted drawback, however, is the relatively large footprint of the antenna, which may pose integration challenges for space-constrained vehicular platforms or dense onboard systems. In [23], the authors propose a compact and wearable LoRa patch antenna specifically engineered for wireless body area networks (WBANs), targeting vital sign monitoring such as heart rate and body temperature. Operating at 868 MHz and 915 MHz, the design addresses the scarcity of sub-1 GHz wearable antennas in current research. The antenna, fabricated on a Rogers Duroid RO3003 substrate (Rogers Corporation, Chandler, AZ, USA), incorporates a U-slot, matching stub, and partial ground plane to enhance impedance matching and efficiency. With dimensions of 80 \(\times\) 60 mm\({}^{2}\) (approximately 0.23\(\lambda_{0}\)\(\times\) 0.17\(\lambda_{0}\)), it achieves a peak gain of 2.12 dBi and an exceptional radiation efficiency of 99.8% at 868 MHz, maintaining robust performance under bending conditions. SAR analysis confirms its compliance with FCC and ICNIRP safety guidelines. Design optimization was conducted using a simulator, supported by a supervised ensemble regression machine learning model to predict the resonance frequency from geometric parameters. The model achieved an R\({}^{2}\) score of 87.68%, offering a notable reduction in reliance on full-wave simulations. Real-world testing with a LoRa SX1276 (Sentech, Camarillo, CA, USA) transceiver demonstrated an average RSSI improvement of \(-\)5 dBm over distances up to 1 km, validating the antenna's suitability for long-range biomedical telemetry. A potential limitation, however, lies in the moderate prediction accuracy (R\({}^{2}\) = 87.68%), which may be insufficient for highly sensitive applications or varying environmental/user scenarios. In another study, a compact terahertz (THz) antenna was developed for next-generation wireless and internet of things (IoT) applications, combining full-wave and circuit-based simulations with machine learning-driven optimization. The antenna, measuring only 120 \(\times\) 200 \(\upmu\)m\({}^{2}\) and fabricated on a polyimide substrate, was modeled in CST Microwave Studio and achieved a peak gain of 12.12 dB, isolation above 36 dB, and radiation efficiency of 88.86% across a wide operational bandwidth of 2.6 THz (7.24-9.84 THz). To validate the design, an equivalent RLC circuit was simulated in a simulator, demonstrating strong agreement with CST-based S-parameters. A variety of supervised regression ML algorithms were applied to predict isolation characteristics, with gradient-boosting regression emerging as the most effective model, achieving an R\({}^{2}\) score above 94% and superior performance in terms of MSE, MAE, and RMSE. This approach significantly reduced simulation time and improved optimization accuracy. Nonetheless, the study's major limitation lies in its exclusive reliance on simulated data from a single design configuration, without any experimental verification, raising concerns about the generalizability of the proposed ML models across different fabrication scenarios. Despitethis, the integration of ML into THz antenna design showcases great promise for the rapid development of high-performance solutions in ultrafast wireless systems [24].