Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance

Abstract

The performance of wireless 5 G communication networks can be enhanced by combining multiple-input multiple-output (MIMO) antennas with machine learning (ML). The suggested antenna, which is constructed on a Rogers 5880 substrate, is well-suited for usage in the high bands of 5 G applications due to its 27 dB isolation and bandwidth of 35.181–39.689 (4.508) GHz within a -10 dB range. Besides being compact (measuring 37 mm × 24 mm), it boasts an impressive maximum gain of 8.09 dB and an efficiency level of 98.2 %. The methods explored in this research are the RLC equivalent circuit model and simulations with validated measurements. An advanced design system (ADS) is utilized to compare the return loss because of CST to create a model like the suggested MPA. The next step is extensive data sampling using CST MWS simulation, followed by applying supervised regression ML techniques. Lasso regression yields excellent results in terms of accuracy and achieves the lowest degree of error when testing the bandwidth prediction. Considering everything, the antenna stands out as a top choice for the 5 G communication system's high band. Designing a small MIMO antenna for 38 GHz mm-wave 5 G applications presents challenges because it requires balancing high performance while minimizing mutual coupling between closely spaced elements and dealing with high-frequency complexities.