Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication

Abstract

Abstract In this article, a compact dual port Multiple Input Multiple Output (MIMO) Coplanar Waveguide (CPW) fed Ultra-Wideband (UWB) antenna for the next generation wireless communication using Machine Learning (ML) optimization is presented. It is designed on an FR4 epoxy substrate of 16 × 30 mm2 with a thickness of 1.6 mm. A bandwidth of 8.7 GHz (2.78–11.48 GHz) is achieved. It is used for 5G New Radio Bands (n78/n46/n47/n77/n48/ n79/n96), Wi-Fi 5, DSRC, Wi-Fi 6, and Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), and Vehicle to Network (V2N) in the entire operating band. The proposed antenna is optimized through the different ML algorithms Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The DT ML algorithms provide a higher accuracy of 99.92% compared to the remaining ML algorithms. A test and fabrication of the suggested antenna is also done. The findings showed that there was a good correlation between measurement and simulation data for several parameters, including S-parameters, radiation patterns, and MIMO parameters like diversity gain (DG), channel capacity loss (CCL), mean effective gain (MEG), envelope correlation coefficients (ECC), and total active reflection coefficients (TARC). Hence, it is suitable for next-generation wireless communication.