A Deep Learning and NSGA-II Based Multi-Objective Optimization Framework for SAR-Aware AMC-Backed Wearable Antenna Design

Authors

  • Muthukumaravel K Department of Electronics and Communication Engineering, Meenakshi Ramaswamy Engineering College, Thathanur, Ariyalur, Tamil Nadu, India
  • Suganthi Santhanam Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Samayapuram, Trichy, Tamil Nadu, India

DOI:

https://doi.org/10.13052/2026.ACES.J.410704

Keywords:

5G sub-6 GHz, artificial magnetic conductor (AMC), deep learning, fractal geometry, machine learning, NSGA-II, SAR-aware design, wearable antenna

Abstract

This paper presents a SAR-aware machine-learning framework for optimizing an AMC-backed triple-band wearable antenna based on a two-iteration Sierpinski carpet radiator operating at 1.82, 2.42, and 5.00 GHz. A 600-sample full-wave CST dataset varies feed length, AMC-to-antenna gap, fractal scale, and AMC inter-cell gap. A multi-task DNN maps four normalized inputs to twelve outputs: gain, resonance-frequency deviation, SAR1g, and SAR10g across all bands. NSGA-II then identifies Pareto-optimal gain–SAR trade-offs, while SHAP indicates feed length and AMC gap dominate SAR. The optimized design achieves gains of 4.12/4.25/4.38 dBi and SAR of 0.082/0.075 W/kg, satisfying IEEE C95.1 and ICNIRP limits and improving over 2.6–2.9 dBi standalone gain values.

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Author Biographies

Muthukumaravel K, Department of Electronics and Communication Engineering, Meenakshi Ramaswamy Engineering College, Thathanur, Ariyalur, Tamil Nadu, India

Muthukumaravel K received the Bachelor of Engineering degree in Electronics and Communication Engineering from Annamalai University, Chidambaram, Tamil Nadu, India, and the Master of Engineering degree in Applied Electronics from Anna University, Chennai, Tamil Nadu, India. He has more than 12 years of academic experience and is currently serving as an Assistant Professor in the Department of Electronics and Communication Engineering at Meenakshi Ramaswamy Engineering College, Thathanur, Ariyalur, Tamil Nadu, India. His research interests include antenna design and related RF communication systems. He has actively participated in several faculty development programs and completed multiple NPTEL courses.

Suganthi Santhanam, Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Samayapuram, Trichy, Tamil Nadu, India

Suganthi Santhanam received the Bachelor of Engineering degree in Electronics and Communication Engineering from Bharathidasan University, Tiruchirappalli, Tamil Nadu, India, the Master of Engineering degree in Computer and Communication Engineering from Anna University, Chennai, Tamil Nadu, India, and the Ph.D. degree in RF MEMS from Anna University, Chennai. She has more than 25 years of academic experience and is currently serving as a Professor in the Department of Electronics and Communication Engineering at K. Ramakrishnan College of Technology, Tamil Nadu, India. Her research interests include artificial neural networks, digital signal processing, MATLAB-based modeling, computer-aided design of VLSI circuits, and microwave modeling methodologies. She has published more than 30 research articles in SCI- and Scopus-indexed journals and has received research funding from AICTE and ICMR. She has actively participated in and organized several faculty development programs, including Anna University-sponsored FDPs. She is currently guiding research scholars in the areas of wireless sensor networks, image processing, RF device design, photonic networks, and VLSI design.

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Published

2026-07-30

How to Cite

[1]
M. . K and S. . Santhanam, “A Deep Learning and NSGA-II Based Multi-Objective Optimization Framework for SAR-Aware AMC-Backed Wearable Antenna Design”, ACES Journal, vol. 41, no. 07, pp. 635–649, Jul. 2026.

Issue

2026: Vol 41 Iss 7

Section

General Submission

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