5G Antenna Using Machine Learning Algorithms

Industry 4.0 is going to combine emerging technologies such as artificial intelligence, IoT, cloud computing, 5G, and many more. The 5G mobile communication is essential to Industry 4.0 connectivity requirements such as low latency (less than 1ms), high data rate (20 Gbps), high reliability, high bandwidth (upto 400 MHz), and connected massive number of devices. The 5G also plays a significant role in the advancement of crucial sectors such as healthcare for robotic surgery, defense for faster and secure communication, and smart cities for enabling drive-less cars. The crucial part of 5G mobile communication is millimeter wave antenna (mmWave). In this paper the the 5G single input single output (SISO) antenna is design for 28 GHz millimeter wave (mmWave) wireless communication. The proposed antenna structure covers the mmWave 5G NR frequency bands n-257 (26.5–29.5 GHz) and n261 (27.5–28.35 GHz). The prediction of scattering parameters (S11) is achieved through machine learning (ML) models such as Gaussian process regression (GPR), random forest regression (RFR), and gradient booster (GBR) regression. The trained ML models take less time in return-loss prediction of 5G antenna at 28 GHz frequency and have low complexity as compared to traditional electromagnetic simulator software. The prediction of RFR model is marginally greater than other models. The root mean square error (RMSE) reported is 1.08.