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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 learn...
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Published in: | IEEE access 2021, Vol.9, p.145583-145591 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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 |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2021.3123219 |