Machine Learning-Driven Design and Experimental Validation of a Highly Miniaturized Dual-Band MIMO Antenna for Sub-6 GHz Applications.

The rapid expansion of sub-6 GHz 5G and Internet of Things (IoT) networks demands highly miniaturized Multiple-Input Multiple-Output (MIMO) antennas. However, balancing extreme physical compactness with rigorous inter-port isolation introduces severe computational bottlenecks for conventional optimization algorithms. To overcome these multidimensional challenges, this paper proposes a novel Deep Surrogate Active Learning framework for the autonomous design and empirical validation of an ultra-compact dual-band MIMO antenna. By using a surrogate-assisted closed-loop strategy to reduce reliance on repeated full-wave evaluations, the methodology combined a custom-penalized Deep Neural Network with dynamic boundary reduction. After training the initial surrogate model with 440 valid full-wave responses obtained from the offline design-of-experiments (DOE) stage, the best CST-validated candidate was identified at the 83rd active learning cycle. The optimized nested-loop geometry, incorporating a partial defected ground structure (DGS), occupies an extremely confined footprint of only 1634 mm