Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks
This study explores the development of a 28 GHz array antenna with beam-steering capability, consisting of four elements with dual linear polarization at ±45 degrees. We propose a method for synthesizing the array antenna’s radiation pattern using an active element pattern-deep...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10981632/ |
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| author | Siti Zainab M. Zainab Hamzah Norun Farihah Abdul Malek Sarah Yasmin Mohamad Farah Nadia Mohd Isa Teddy Surya Gunawan Kuo-Sheng Chin |
| author_facet | Siti Zainab M. Zainab Hamzah Norun Farihah Abdul Malek Sarah Yasmin Mohamad Farah Nadia Mohd Isa Teddy Surya Gunawan Kuo-Sheng Chin |
| author_sort | Siti Zainab M. Zainab Hamzah |
| collection | DOAJ |
| description | This study explores the development of a 28 GHz array antenna with beam-steering capability, consisting of four elements with dual linear polarization at ±45 degrees. We propose a method for synthesizing the array antenna’s radiation pattern using an active element pattern-deep neural network (AEP-DNN). Beam-steering has become an attractive feature for researchers, as it enables users to move freely without affecting signal strength. An array analysis was conducted using a feedforward deep neural network (DNN) to generate a radiation pattern that achieves the desired steering angles. The proposed method takes radiation patterns as inputs and outputs the corresponding phase values for the antenna elements. The training dataset for the array antenna consisted of 6,859 radiation patterns, generated by adjusting the antenna element phases, which were then used to train the DNN model with minimal complexity. The radiation pattern was computed using AEP method since it is faster and less complex compared to full-wave modelling methods. The DNN model was initially tested using radiation patterns from an ideal square shape. After training, the model was evaluated by inserting desired beam-steering angles of 5 and 10 degrees, and it was found that the radiation pattern produced by the DNN closely matched the intended input pattern. The DNN learning process takes approximately 2 to 3 minutes in terms of processing time. The training and validation Root Mean Square Error (RMSE) and loss values converge to a minimum range of 1.3 to 2.3. Furthermore, the AEP-DNN method was successfully validated using the pattern multiplication method, full-wave modelling, and measurement methods to verify the feasibility and reliability of the training and validation data, as well as the resulting radiation pattern. This antenna, incorporating AEP-DNN technology, holds significant potential for various applications, particularly in mobile communications. |
| format | Article |
| id | doaj-art-e46a9b3461ac40448b2a48cc0f27edb4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e46a9b3461ac40448b2a48cc0f27edb42025-08-20T02:31:12ZengIEEEIEEE Access2169-35362025-01-0113806808069410.1109/ACCESS.2025.356625210981632Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless NetworksSiti Zainab M. Zainab Hamzah0https://orcid.org/0009-0005-4456-9893Norun Farihah Abdul Malek1https://orcid.org/0000-0002-9234-4139Sarah Yasmin Mohamad2https://orcid.org/0000-0002-4768-9664Farah Nadia Mohd Isa3https://orcid.org/0000-0002-4305-4151Teddy Surya Gunawan4https://orcid.org/0000-0003-3345-4669Kuo-Sheng Chin5https://orcid.org/0000-0003-3297-4224Department of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, MalaysiaDepartment of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, MalaysiaDepartment of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, MalaysiaDepartment of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, MalaysiaDepartment of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, MalaysiaDepartment of Electronic Engineering, Chang Gung University, Guishan District, Taoyuan, TaiwanThis study explores the development of a 28 GHz array antenna with beam-steering capability, consisting of four elements with dual linear polarization at ±45 degrees. We propose a method for synthesizing the array antenna’s radiation pattern using an active element pattern-deep neural network (AEP-DNN). Beam-steering has become an attractive feature for researchers, as it enables users to move freely without affecting signal strength. An array analysis was conducted using a feedforward deep neural network (DNN) to generate a radiation pattern that achieves the desired steering angles. The proposed method takes radiation patterns as inputs and outputs the corresponding phase values for the antenna elements. The training dataset for the array antenna consisted of 6,859 radiation patterns, generated by adjusting the antenna element phases, which were then used to train the DNN model with minimal complexity. The radiation pattern was computed using AEP method since it is faster and less complex compared to full-wave modelling methods. The DNN model was initially tested using radiation patterns from an ideal square shape. After training, the model was evaluated by inserting desired beam-steering angles of 5 and 10 degrees, and it was found that the radiation pattern produced by the DNN closely matched the intended input pattern. The DNN learning process takes approximately 2 to 3 minutes in terms of processing time. The training and validation Root Mean Square Error (RMSE) and loss values converge to a minimum range of 1.3 to 2.3. Furthermore, the AEP-DNN method was successfully validated using the pattern multiplication method, full-wave modelling, and measurement methods to verify the feasibility and reliability of the training and validation data, as well as the resulting radiation pattern. This antenna, incorporating AEP-DNN technology, holds significant potential for various applications, particularly in mobile communications.https://ieeexplore.ieee.org/document/10981632/Dual-polarized antenna arraysmillimeter-wavesbeam-steering28 GHz frequencyactive element patterndeep neural network |
| spellingShingle | Siti Zainab M. Zainab Hamzah Norun Farihah Abdul Malek Sarah Yasmin Mohamad Farah Nadia Mohd Isa Teddy Surya Gunawan Kuo-Sheng Chin Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks IEEE Access Dual-polarized antenna arrays millimeter-waves beam-steering 28 GHz frequency active element pattern deep neural network |
| title | Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks |
| title_full | Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks |
| title_fullStr | Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks |
| title_full_unstemmed | Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks |
| title_short | Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks |
| title_sort | deep learning driven beam steering for dual polarized 28 ghz antenna arrays in 5g wireless networks |
| topic | Dual-polarized antenna arrays millimeter-waves beam-steering 28 GHz frequency active element pattern deep neural network |
| url | https://ieeexplore.ieee.org/document/10981632/ |
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