A Comparative Study of Anti-Jamming Beamforming Using Deep Learning in Planar Phased Array Antennas

Authors

  • Aymen Alhamdan Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
  • Lotfi Laadhar Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
  • Mohammed Aseeri Next Generation Connectivity and Wireless Sensors Institute King Abdulaziz City of Science and Technology (KACST), Riyadh, Saudi Arabia
  • Abdullah Dobaie Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
  • Hatem Rmili Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

DOI:

https://doi.org/10.13052/2026.ACES.J.410401

Keywords:

Anti-jamming, antenna arrays, deep learning, beamforming

Abstract

In this study, a deep learning-based beamforming comparative study for anti-jamming applications in 2D-planar phased arrays is presented. For better array architecture benchmarking, three different geometries (circular, rectangular, hexagonal) are considered. Convolutional Neural Network (CNN) is employed to translate a target radiation pattern, generated as an image, directly into the optimal antenna currents. Adaptive antenna array beamforming weights can be estimated efficiently by the deep learning-based MATLAB code according to the desired beam steering angle and the null direction of the jammer. This approach establishes a smart, non-iterative mapping that bypasses traditional optimization algorithms, reducing computation time by up to 260x. Once trained, the model delivers optimal currents and weights in a single and efficient forward pass.

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Author Biographies

Aymen Alhamdan, Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

Aymen Alhamdan received the M.Sc. degree in electronic and electrical engineering from Strathclyde University, Glasgow, UK, in 2011. He is currently working toward the Ph.D. degree in Electronic and Computing Engineering at King Abdulaziz University, Jeddah, Saudi Arabia. His research interests are antenna array design and deep learning.

Lotfi Laadhar, Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

Lotfi Laadhar received his Engineering and Ph.D. degrees in Telecommunications from High Institute of Communications of Moscow, Russia, in 1985. From 1990 to 2001, he was Assistant Professor at the Air Force Academy in Tunis, Tunisia. From 2001 to 2012, he was Assistant Professor at College of Telecommunications of Jeddah. September 2012 to June 2015, he was Associate Professor at King Abdulaziz University-North Jeddah Branch College of Engineering. He became Electrical Engineering Department Chair in September 2015 and Associate Professor, King Abdelaziz University College of Engineering, Electrical Engineering Department. His research activities include wireless protocols, management of VSAT (Very Small Aperture Terminal) services such as IP connect and IP access, including internet through the satellite (one-way and the 2-way), in addition to antennas and metasurfaces.

Mohammed Aseeri, Next Generation Connectivity and Wireless Sensors Institute King Abdulaziz City of Science and Technology (KACST), Riyadh, Saudi Arabia

Mohammed Aseeri is currently Full Professor at King Abdulaziz City for Science and Technology (KACST), Saudi Arabia, working within the National Center for Radar and Electronic Technology, and serves as Co-Principal Investigator at the Center of Excellence for Microwave Sensor Technology (CMST), a joint initiative between KACST and the University of Michigan, USA. He received his Bachelor’s and M.Sc. degrees in Electrical and Computer Engineering (Electronics and Communications) from King Abdulaziz University, and his Ph.D. in Electronics from the University of Kent, Canterbury, UK. He is a certified Consultant Engineer by the Saudi Council of Engineers (SCE) and holds a Project Management Professional (PMP) certification from PMI. He has gained international research experience through his work as a researcher at the Australian National University (ANU) and the University of Canberra (UC). His professional experience includes managing and supervising advanced electronic surveillance systems and leading multiple national-level projects. His research interests focus on radar systems, electronic warfare, signal processing, wireless sensor networks (WSN), and AI-integrated intelligent sensing systems, including anti-jamming techniques and advanced beamforming for phased array antennas. He is also actively engaged in innovation, technology localization, and the development of smart IoT-based solutions for environmental monitoring and infrastructure protection. He is a senior member of IEEE and IET and has authored numerous scientific publications in highimpact journals and international conferences. He also holds several patents and contributes actively to advancing research, innovation, and strategic technology development.

Abdullah Dobaie, Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

Abdullah Dobaie received his B.Sc. in 1981 and M.Sc. in 1989, both in Electronic and Communication Engineering from King Abdulaziz University in Saudi Arabia, and Ph.D. in 1995 from Colorado State University, USA. He has supervised many masters and doctoral students in electrical and communication area and has directed many projects concerning communication, digital filters, antenna, and digital signal processing. His recent interests include adaptive communication systems, digital image processing, wave propagation, and communication networks.

Hatem Rmili, Faculty of Engineering, Electrical and Computer Engineering Department King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

Hatem Rmili (Senior Member, IEEE) received the B.S. degree in physics from the Science Faculty of Monastir, Tunisia, in 1995, and the DEA diploma (Master) from the Science Faculty of Tunis, in quantum mechanics, in 1999. He received the Ph.D. degree in physics (electronics) from the University of Bordeaux 1, France, in 2004. From December 2004 to March 2005, he was a research assistant in the PIOM laboratory at the University of Bordeaux 1. From March 2005 to March 2007, he was a Postdoctoral Fellow at the Rennes Institute of Electronics and Telecommunications. From March to September 2007, he was a Postdoctoral Fellow at the ESEO engineering school, in Angers. From September 2007 to August 2012, he was an assistant professor with the Mahdia Institute of Applied Science and Technology (ISSAT), Department of Electronics and Telecommunications, Tunisia. He was an Assistant/Associate/Full Professor (from 2012 to 2024) with the Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia. In March 2024, Rmili joined Prince Sultan Defense Studies and Research Center (PSDSARC), Riyadh, as Radar Systems Expert. Rimli’s research interests concern applied electromagnetic applications involving radar, antennas, AESA, metamaterials, and metasurfaces. The main targeted applications are reconfigurable antennas for multi-standard wireless communications systems, security of chipless RFID systems with fractal tags, terahertz photoconductive antennas for infra-red energy harvesting, UWB nano rectennas for collection of solar energy, phase shifters for low-cost 5G communication systems, radar beamforming, high power microwave, and microwave absorbing materials for stealth technologies.

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Published

2026-04-30

How to Cite

[1]
A. . Alhamdan, L. . Laadhar, M. . Aseeri, A. . Dobaie, and H. . Rmili, “A Comparative Study of Anti-Jamming Beamforming Using Deep Learning in Planar Phased Array Antennas”, ACES Journal, vol. 41, no. 04, pp. 297–305, Apr. 2026.

Issue

2026: Vol 41 Iss 4

Section

General Submission

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