Hussein Ezzeddine defends his PhD on Dec. 01, 2022 at 9:30AM.
Place : Ecole Centrale de Lyon, amphithéâtre 3 (bâtiment W1), Ecully
Jury :
Rapporteurs : CARVALHO Nuno Borges (Universidade de Aveiro, Portugal) et TARAJ Robert (Sophia Antipolis)
Examinateur/trice : DENIAU Virginie (Lille), VILLEMAUD Guillaume (INSA de Lyon)
Directeurs de thèse : BREARD Arnaud et DUROC Yvan (Laboratoire Ampère)
Encadrant : Huillery Julien (Ampère)
Abstract :
This work investigates the problem of waveform design and optimization for passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) systems.
Passive UHF RFID is a wireless identification technology widely deployed in various applications. A passive RFID tag depends entirely on the electromagnetic waves transmitted by the reader to power itself via its energy harvester; and responds by backscattering. Although communications with a range of several meters are feasible, the passive nature limits the performance, especially in complex environments where signals are degraded by multipath. Experiments have shown that the use of intermittently pulsed waveforms improves the RF-to-DC power conversion efficiency of the energy harvester, thus increasing the communication range with passive commercial tags. With this in mind, this thesis aims to develop strategies to design optimized waveforms that are adaptive to the channel state information (CSI) and insure high RF-to-DC power conversion efficiency (PCE) at the tag. Knowing that the design of such waveforms requires some knowledge of the channel state at the transmitter, this work aims to answer the question of CSI acquisition at the transmitter by developing a channel learning method compatible with passive UHF RFID systems.
A linear time-invariant (LTI) model of passive UHF RFID systems is proposed. A channel learning method compatible with passive UHF RFID systems is developed based on the LTI model. Strategies to design channel-adaptive waveforms are then formulated. The waveforms designed are based on multisine signals and aim to increase the RF-to-DC power conversion efficiency of the tag’s energy harvester.
In this context, an adapted hybrid (electromagnetic-circuit) simulation model is developed specifically to study waveform design and optimization. The performance of the designed waveforms, and other state-of-the-art waveforms, is evaluated in multiple wireless channel scenarios using the developed simulation model. Two performance evaluation metrics are defined: energy-related, which is based on the DC power collected at the output of the energy harvesting circuit; and information-related, which is based on the baseband information signal backscattered by the tag and detected at the reader. The performance evaluation of the passive UHF RFID radio link has shown that the designed waveforms, especially those adaptive to the CSI, lead to significant improvement in terms of energy- and information-related performance metrics over the non-adaptive waveforms.
Keywords :
UHF RFID, pulsed-mode, passive tag, wireless power transfer, system modelling, channel learning, waveform design, time reversal, multisine.