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- W3210506928 abstract "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." @default.
- W3210506928 created "2021-11-08" @default.
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- W3210506928 date "2021-01-01" @default.
- W3210506928 modified "2023-10-14" @default.
- W3210506928 title "Deep Learning Based Antenna Design and Beam-Steering Capabilities for Millimeter-Wave Applications" @default.
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- W3210506928 doi "https://doi.org/10.1109/access.2021.3123219" @default.
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