# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction
### Related Work
In [25], a compact and high-performance MIMO antenna operating at 38 GHz is proposed for mmWave 5G applications, with the integration of machine learning (ML) techniques to enhance design accuracy and prediction capability. Fabricated on a Rogers 5880 substrate, the antenna achieves strong isolation (27 dB), wide bandwidth (35.181-39.689 GHz), a peak gain of 8.09 dB, and high efficiency (98.2%). The design approach combines RLC equivalent circuit modeling in ADS with full-wave simulation in CST MWS, supported by measurements for validation. A supervised ML framework is developed using data generated from CST, with Lasso regression proving the most accurate for predicting bandwidth. However, a significant limitation arises from the difficulty of maintaining high isolation and low coupling in compact mmWave layouts, where minor fabrication variations can drastically affect performance. In [26], a compact MIMO antenna designed for 28 GHz 5G networks is presented, employing a hybrid approach combining simulations, measurements, and machine learning. With dimensions of 3.19 \(\lambda_{0}\)\(\times\) 3.19 \(\lambda_{0}\), the antenna achieves a wide 5.1 GHz bandwidth, a high gain of 9.43 dBi, excellent isolation of 31.37 dB, and radiation efficiency of 99.6%. The CST-based simulations are validated through measurements and equivalent circuit analysis in ADS. To expedite gain prediction and design cycles, nine ML models are tested, with random forest regression reaching 99% prediction accuracy. Despite its outstanding performance, the relatively large physical footprint presents a key drawback for integration into compact or portable 5G modules at mmWave frequencies. In [27], a compact MIMO antenna is presented, leveraging Carrel's log-periodic design principles in combination with a planar log-periodic array (PLPA) to support ultra-wideband performance across multiple bands, including C, Ku, K, Ka, and Q. The antenna dimensions are 54 \(\times\) 44 \(\times\) 1.58 mm\({}^{3}\). Thanks to the self-multi-band coupling counteraction mechanism, the structure offers high gain (7.21-9.53 dBi) and strong intrinsic isolation (21.54 dB) without the use of external decoupling elements, making it attractive for compact wideband MIMO systems. Its operating bands range from 4.4 GHz to 39.55 GHz, covering multiple key communication ranges. The symmetrical and single-feed topology simplifies the structure but may limit adaptability under asymmetric deployment scenarios. Moreover, the validation is based primarily on simulations with limited measurements, necessitating further real-world testing across all bands to confirm robustness. In [28], the authors proposed a compact and high-performance MIMO antenna for 6G terahertz (THz) applications, employing a stacked ensemble machine learning model to accurately predict and optimize antenna parameters. The design achieved dual-band resonance, excellent isolation, high gain, and remarkable efficiency, making it a strong candidate for future high-speed wireless systems. However, a potential limitation of this work lies in the absence of experimental validation. While full-wave CST simulations and RLC circuit modeling with ADS verification are used, the lack of fabricated prototypes prevents confirmation of performance under real-world conditions, where fabrication tolerances, material losses, and environmental factors at THz frequencies could significantly affect results. Furthermore, the generalization capability of the machine learning model may be limited, as details regarding dataset diversity, feature robustness, and model interpretability are not fully discussed. The authors of [29] propose miniaturized mmWave MIMO antenna arrays operating in the 25.785-29.485 GHz band, achieving a 10 dB bandwidth of 3.7 GHz. Using Rogers RT/Duroid 5880 for fabrication, the design maintains compact dimensions (3.3 \(\lambda_{0}\)\(\times\) 3.3 \(\lambda_{0}\)) while offering a high gain of 8.9 dB, isolation greater than 29.24 dB, and maximum efficiency of 98.4%.