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Rectenna Design Optimized by Binary Genetic Algorithm for Hybrid Energy Harvesting Applications Across 5G Sub-6 GHz Band

  • Ahmed Rifaat Hamad
  • , Ammar Al-Adhami
  • , Nouf Abd Elmunim
  • , Mohammad Alibakhshikenari
  • , Bal Virdee
  • , Hasan Salman Hamad
  • , Renu Jayanthi
  • , Dion Mariyanayagam
  • , Innocent Lubangakene
  • , Sunil Kumar
  • , Salahuddin Khan
  • , Yi Tang
  • , Lida Kouhalvandi
  • , Taha A. Elwi
  • , Mohsin Ali Ahmed
  • , Nasr Rashid
  • Middle Technical University
  • Al-Karkh University of Science
  • Princess Nourah Bint Abdulrahman University
  • Electronics Engineering Department
  • University of Rome "Tor Vergata"
  • London Metropolitan University
  • Al-Bayan University
  • King Saud University
  • Yantai University
  • Dogus University
  • Al-Nahrain University
  • International Applied and Theoretical Research Center (IATRC)
  • Jouf University
  • Faculty of Engineering, Al-Azhar University

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

12 Citations (Scopus)

Abstract

This paper presents a novel rectenna design for hybrid energy harvesting, optimized using a binary genetic algorithm (BGA) with binary coding to improve geometry, impedance matching, and radiation efficiency. The fabricated rectenna achieves reflection coefficients below −40 dB at 2.45 and 5.8 GHz, demonstrating excellent impedance matching. A commercial rectifier (Powercast P21XXCSR-EVB), employing a voltage doubler topology and Schottky diodes (Skyworks SMS7630 and Avago HSMS 285B), is integrated for RF-to-DC conversion. Peak efficiencies of 90% at 2.45 GHz and 52% at 5.8 GHz are recorded at 11 dBm input power, while efficiencies above 80% and 50%, respectively, are maintained at 0 dBm. The rectifier also exhibits wide impedance bandwidths, with reflection coefficients of −23 dB and −18 dB at the respective frequencies. Outdoor testing yields DC output voltages of 92.6 mV (2.45 GHz) and 64 mV (5.8 GHz). The system's efficiency and adaptability under variable conditions make it ideal for low-power applications such as wireless sensor networks, Internet of Things devices, and remote monitoring. Its robust performance across environments highlights its potential for autonomous energy harvesting in 5G and sub-6 GHz networks.

Original languageEnglish
Article numbere2024RS008154
JournalRadio Science
Volume60
Issue number6
DOIs
Publication statusPublished - Jun 2025
Externally publishedYes

Keywords

  • 5G and sub-6 GHz
  • artificial intelligence (AI)
  • binary coding
  • binary genetic algorithm (BGA)
  • energy harvesting
  • rectenna design

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