Antenna Array Pattern with Sidelobe Level Control using Deep Learning

Authors

  • Muhammad A. Abdullah Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan
  • Alam Zaib Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan
  • Shafqat U. Khan Department of Electronics University of Buner, Buner, KPK 19290, Pakistan
  • Shoaib Azmat Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan
  • Shahid Khattak Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan
  • Benjamin D. Braaten Department of Electrical and Computer Engineering North Dakota State University, Fargo, ND 58102, USA
  • Irfan Ullah Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan https://orcid.org/0000-0001-8909-2447

DOI:

https://doi.org/10.13052/2025.ACES.J.400506

Keywords:

AESA antennas, array pattern, deep learning, deep neural network, sidelobe level control

Abstract

Motivated by the demonstrated success of artificial intelligence (AI) in wireless communications systems, this paper proposes a deep learning-based approach for generating a desired radiation pattern with sidelobe level (SLL) control in active electronically scanned array (AESA) antennas. Recent works in this direction are mostly limited to generating radiation patterns with only beam scanning capability, inhibiting their wide-scale applicability. In this work, we propose a unified deep neural network (DNN) model that enable simultaneous control over both beam scanning angles and SLLs across a range of operating scenarios. To accomplish this task, the DNN model efficiently predicts the phase and amplitude of each array element. To learn the DNN model’s parameters, we construct a training dataset comprising amplitude values and phases as labeled outputs and corresponding 181-point radiation patterns as input features. The training and validation process of the proposed DNN model reveals high accuracy in terms of R2 score and mean square error (MSE). For prediction, the desired radiation pattern consisting of 181 points is fed to the trained DNN model to yield optimized weights of antenna elements. The numerical results on a 1×8 linear phase antenna array, using an assortment of beam scanning angles and SLLs, demonstrate the effectiveness of the proposed model. The numerical results presented in MATLAB and CST simulators are validated by measurements on a 1×8 microstrip prototype array.

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Author Biographies

Muhammad A. Abdullah, Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan

Muhammad A. Abdullah holds a bachelor’s degree in Computer Engineering from COMSATS University Islamabad, Pakistan. He is currently an AI Researcher and Engineer at ADK Technology Co., specializing in the development of machine learning (ML) and deep learning (DL) models for industrial applications, including neural architecture optimization and deployment of generative AI systems.

Alam Zaib, Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan

Alam Zaib received the Ph.D. degree in Electrical Engineering from KFUPM, Dhahran, Saudi Arabia, in 2016. He was an Erasmus Mundus scholar in MERIT master program from 2007 to 2009. Currently he is Associate Professor in the Department of Electrical Engineering at COMSATS University Islamabad, Abbottabad Campus. His research interests are in signal processing, wireless communications and applications of AI and machine learning in antenna arrays and wireless communication.

Shafqat U. Khan, Department of Electronics University of Buner, Buner, KPK 19290, Pakistan

Shafqat U. Khan received M.S. and Ph.D. degrees in Electronic Engineering from International Islamic University Islamabad and ISRA University in 2008 and 2015, respectively. He was a Post Doc Fellow at the Faculty of Electrical Engineering, University Technology Malaysia, from 2016 to 2017. He is an Associate Professor at the University of Buner. His research interest includes the RF & microwave, antenna arrays and evolutionary algorithms.

Shoaib Azmat, Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan

Shoaib Azmat received his Ph.D. and M.S. degrees in Electrical and Computer Engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2014 and 2011, respectively. Currently, he is an Associate Professor of Computer Engineering at COMSATS University, Abbottabad. His research interests include computer vision, machine learning, and digital image processing, with research publications in reputed international journals.

Shahid Khattak, Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan

Shahid Khattak received Dr.-Ing degree from Technische Universität Dresden, Germany, in 2008. He is a distinguished academic and researcher specializing in electrical and computer engineering with a focus on advanced communication systems, signal processing, and applied electromagnetics. He has contributed extensively to both theoretical and applied research, with numerous publications in reputed international journals. Khattak is known for his dedication to academic excellence and mentoring, and he plays a pivotal role in advancing interdisciplinary research in engineering and technology.

Benjamin D. Braaten, Department of Electrical and Computer Engineering North Dakota State University, Fargo, ND 58102, USA

Benjamin D. Braaten (Senior Member, IEEE) received the B.S. degree in electrical engineering, the M.S. degree in electrical engineering, and the Ph.D. degree in electrical and computer engineering from North Dakota State University, Fargo, ND, USA, in 2002, 2005, and 2009, respectively. During the 2009 Fall semester, he held a postdoctoral research position with the South Dakota School of Mines and Technology, Rapid City, SD, USA. His research interests include printed antennas, conformal self-adapting antennas, microwave devices, topics in EMC, topics in BIO EM, and methods in computational electromagnetics He is currently a Chairman of the ECE Department at NDSU, Fargo, ND, USA. He has authored or coauthored more than 100 reviewed journal and conference publications, several book chapters and holds one U.S. patent on wireless pacing of the human heart.

Irfan Ullah, Department of Electrical and Computer Engineering COMSATS University, Abbottabad, KPK 22060, Pakistan

Irfan Ullah received a Ph.D. degree in Electrical and Computer Engineering from North Dakota State University, Fargo, ND, USA, in 2014. He is an Associate Professor in Electrical Engineering Department at COMSATS University Islamabad, Abbottabad Campus. His research interests include beamforming arrays, machine learning in antenna arrays, electromagnetic metamaterials, and topics in EMC.

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https://www.ndsu.edu/pubweb/~braaten/dissertation_Irfan.pdf

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Published

2025-05-30

How to Cite

[1]
M. A. . Abdullah, “Antenna Array Pattern with Sidelobe Level Control using Deep Learning”, ACES Journal, vol. 40, no. 05, pp. 427–435, May 2025.

Issue

2025: Vol 40 Iss 5

Section

General Submission