A Learning based Compact Wideband mm Wave Antenna Array Design Optimization for 5G iPhone Mobile Handsets

Abstract

In this paper, a mmWave antenna is designed and optimized by machine learning to meet 5G requirements. A Surrogate based optimization approach is used to optimize this antenna. The goals of this optimization are antenna gain, bandwidth, and size. The bandwidth of the optimized antenna covers the 27.5 GHz- 38.9 GHz frequency band. This antenna has a wide 3 dB beam width with 90^∘ and 178^∘ beam width in the E-plane and H-plane, respectively. The realized gain of the antenna is 5 dB in 28 GHz frequency. One of the benefits of this antenna is Its compact size. The total dimension of the antenna is 5.4×5.6 mm². In addition, an array of this antenna is proposed with 4 elements. The analog beamforming is achieved by means of this array. The 4-element array is simulated in an iPhone package model. Finally, an antenna with a wide beam, wide band, high gain, and small size is achieved within the Ka-band.