Abstract
Traditional optimization methods for antenna design are time-consuming, whereby full-wave electromagnetic (EM) simulations are performed repeatedly to find the optimum design parameters for acceptable performance. However, deep learning (DL) is an effective method for determining the optimum physical parameters based on their complex, nonlinear relationship with the design characteristics. This paper proposes a time and resource-efficient DL approach for designing MIMO antenna arrays. A wideband, eight-element MIMO antenna array for fifth-generation (5G) cellphones is designed to operate at n77/n78/n79 sub-6 GHz new radio (NR) bands and optimized using the proposed approach. The proposed method utilizes the feature reduction method to reduce the design space and generate an effective dataset. A novel dual-channel deep neural network (DC-DNN) is developed to predict the scattering parameters. A direct optimization approach is used by utilizing the defined figure of merit (FOM) for multiple design objectives instead of using an optimization algorithm with the proposed DL model. The optimized MIMO antenna is fabricated and tested for performance evaluation. The optimized design achieved −6 dB impendence bandwidth of 45.5 % (3.28–5.21 GHz), isolation < –12.5 dB, TARC < –6 dB, ECC < 0.1, MEG < −6 dB, and CCL < 0.35 bps/Hz.
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