Machine Learning-Aided Design of Defected Ground Structures for PRGW-Based MIMO Antennas

This article introduces a scalable machine learning (ML)-based inversed design approach of a newly shaped defected ground structure (DGS) for isolation enhancement of a two-port multiple-input and multiple-output (MIMO) antenna based on the printed ridge gap waveguide (PRGW) technology. The proposed approach employs an artificial neural network (ANN) to predict the dimension and position of the proposed DGS in terms of the isolation and gain. As a test case, the proposed approach is applied to a two-port MIMO antenna design with very low-antenna element separation of 0.36λ, enhanced isolation of 30 dB, an average high gain of 8 dBi, and a radiation efficiency of >85%. Following a unique data generation phase of 96 h, the target objectives are computed in ≈ 1 min with a low root mean squared error (RMSE) of 0.02. This is a significant improvement compared with conventional genetic algorithms (GAs) and particle swarm optimization (PSO) available in the commercial CST EM simulator, which require repeated computations of more than 74 and 46 h, respectively. The proposed approach proved to be an attractive choice that can be adapted to design 4×4 and 16×16 as well as more element MIMO systems for Internet-of-Space (IoS) downlink applications.