# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 4 Discussion and Future Opportunities
For example, by adapting the design framework to tissue-mimicking phantoms, this methodology could support applications like the early detection of skin sarcoma, targeted cancer therapy, or real-time health monitoring, areas where low-profile, high-isolation antennas are crucial. Finally, expanding the design pipeline toward multi-objective optimization, balancing gain, isolation, bandwidth, efficiency, and size will be key to meeting the rigorous constraints of next-generation wireless and healthcare applications. The current ML-based framework provides a strong foundation for developing such general-purpose, adaptive antenna systems across domains. Also, in future work, we intend to address the effects of fabrication and measurement uncertainties through a statistical validation framework. Specifically, a \(\pm\)3% variation in \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Input Feature** & **Output Features** & **R\({}^{2}\)** & **RMSE (dB)** & **MAE (dB)** \\ \hline
1 & Lp, Wp, Lf, Wf, Frequency & S11 & 0.9929 & 0.1977 & 0.4201 \\
2a & r1, gx, e1, xl, Frequency & S11 & 0.9850 & 0.7429 & 0.3080 \\
2b & r1, gx, e1, xl, Frequency & S12 & 0.9880 & 0.8418 & 0.4029 \\
3a & ns, yp, Frequency & S11 & 0.999 & 0.0284 & 0.0137 \\
3b & ns, yp, Frequency & S12 & 0.9998 & 0.0924 & 0.0223 \\ \hline \end{tabular}
\end{table} 
Table 4: Machine learning model performance: dataset features and prediction accuracy metrics. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**N.** & **Frequency (GHz)** & **N. of** & **ML Model** & **Gain (dB)** & **Isolation Level** & **Structure Complexity** & **MAE (dB)** \\
**Ref** & **Frequency (GHz)** & **Element** & **ML Model** & **Gain (dB)** & **(dB)** & **Stochastic Complexity** & **MAE (dB)** \\ \hline
[31] & 1.59–2.26, 3.1–3.87, 5.25–6.91 (triple bande) & 2 & RF & 15 & 20 & Complex & MSE = 0.69 \\
[32] & Tunable (wide range) & 2 & KNN & 19 & 19 & Complex & MSE = 0.81 \\
[33] & THz (6G band) & 1 & RF & 6.2 & - & Complex & MSE = 3.81 \\
[33] & THz (6G band) & 1 & RF & 6.2 & - & Complex & R\({}^{2}\) = 0.89 \\
[34] & 28 (mmWave) & 2 & KBNN & 10 & 19 & Simpl & - \\
[35] & 3.10–10.42 (UWB) & 1 & KNN & - & - & Complex & R\({}^{2}\) = 0.99 \\ This work & 26 (mmWave) & 2 & RF & 10.2 & 21 & Simpl & MSE = 0.02 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of related works focusing on ML models and its performance. key antenna dimensions and substrate properties will be introduced in a Monte Carlo simulation environment to quantify the robustness of our ML predictions. This will ensure that small manufacturing imperfections do not significantly degrade antenna performance. Moreover, following the methodology outlined in [36], where real-world exposure to electromagnetic fields was monitored using FFNN models, we aim to implement a similar AI-assisted validation scheme. This approach can help bridge the gap between simulated and measured performance by continuously refining the ML model with experimental data. In addition, as proposed in [37], it would be valuable to extend our framework to include material variability and defect-tolerant modeling, thereby increasing the reliability of the design under practical fabrication conditions. Furthermore, in alignment with the statistical validation strategy described in [38], we plan to incorporate multiple fabricated prototypes and repeatability measurements to strengthen the experimental robustness of our design. This will help to ensure that our ML-based system performs reliably under real-world manufacturing variability. Although our ML-driven design process demonstrated strong prediction accuracy within the defined design space (20-34 GHz, with FR4 substrate of 1.6 mm thickness), it may face limitations when applied outside this scope. The model performance can degrade when dealing with antenna geometries or material properties that differ significantly from those represented in the training data.