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Optimum power transfer in RF front end systems using adaptive impedance matching technique

  • Mohammad Alibakhshikenari
  • , Bal S. Virdee
  • , Leyre Azpilicueta
  • , Chan H. See
  • , Raed Abd-Alhameed
  • , Ayman A. Althuwayb
  • , Francisco Falcone
  • , Isabelle Huynen
  • , Tayeb A. Denidni
  • , Ernesto Limiti
  • Electronics Engineering Department
  • University of Rome "Tor Vergata"
  • London Metropolitan University
  • Instituto Tecnológico de Estudios Superiores de Monterrey
  • Edinburgh Napier University
  • University of Bradford
  • Jouf University
  • Public University of Navarre
  • Université Catholique de Louvain
  • INRS-Océanologie

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

18 Citations (Scopus)

Abstract

Matching the antenna’s impedance to the RF-front-end of a wireless communications system is challenging as the impedance varies with its surround environment. Autonomously matching the antenna to the RF-front-end is therefore essential to optimize power transfer and thereby maintain the antenna’s radiation efficiency. This paper presents a theoretical technique for automatically tuning an LC impedance matching network that compensates antenna mismatch presented to the RF-front-end. The proposed technique converges to a matching point without the need of complex mathematical modelling of the system comprising of non-linear control elements. Digital circuitry is used to implement the required matching circuit. Reliable convergence is achieved within the tuning range of the LC-network using control-loops that can independently control the LC impedance. An algorithm based on the proposed technique was used to verify its effectiveness with various antenna loads. Mismatch error of the technique is less than 0.2%. The technique enables speedy convergence (< 5 µs) and is highly accurate for autonomous adaptive antenna matching networks.

Original languageEnglish
Article number11825
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

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