Deep Learning Based Antenna Design and Beam-Steering Capabilities for Millimeter-Wave Applications

In this study, a deep neural network (DNN) is implemented to soft computation of the dual-band circularly polarized bone-shaped patch antenna (BSPA) at 28 GHz and 38 GHz for 5G applications. Via a simulated database of 150 BSPAs, a DNN model is constructed on a 5-layer system using an adaptive learning rate algorithm. The framework and hyper-parameters of the DNN model are optimized in the training phase of a hybrid algorithm combining strengths of both particle swarm optimization (PSO) and a modified version of the gravitational search algorithm (MGSA-PSO). To generate the database for training and testing the model, 150 BSPAs with different geometrical are simulated in terms of the resonant frequency using a precise electromagnetic analysis platform. A fabricated BSPA operating at 28 GHz and 38 GHz is used to test and verify the DNN model. Then, the application of DNN with back-propagation algorithm and weighted MGSA-PSO algorithm is used for beam-steering the main beam pattern of the designed uniform circular antenna array with side-lobe level <= -30 dB by estimating the appropriate feeding phases of the 16 elements. Several illustrative examples are placed to beam-steer the pattern in the desired direction to check the validity of the technique.