A Machine Learning Based Design of mmWave Compact Array Antenna for 5G Communications

Abstract

Wider impedance bandwidth (IBW), and lower latency rate than older mobile communication systems possess are required for fifth-generation (5G) mobile communication systems. Furthermore, with respect to the high operation frequency of 5G systems, a high released gain is necessary to compensate for the high path loss on these frequencies. With respect to the requirements mentioned above, millimeter-wave (MMW) antennas seem to be a good solution for 5G applications. The low wavelength of MMW frequency bands, makes it practical to use large array antennas for massive multi input multi-output (MIMO) 5G systems with high gain. The high number of design variables of antennas makes an optimum antenna harder to design. Using machine learning (ML) approaches, however, alleviates this challenge. However, most ML approaches entail high computational complexity. Therefore, surrogate-based optimization (SBO) approaches must be used to handle the high computational complexity of ML approaches.