Project Details
Description
By producing new and accurate 'black box' models for RFIC's which describe the linear as well as the nonlinear behaviour of these ICs, it becomes possible to analyse the fell RF circuit.
New measurement and modelling techniques based on the Nonlinear Vectorial Network Analyser
Wendy Van Moer
An Automatic Harmonic Selection Scheme for Measurements and Calibration with the Nonlinear Vectorial Network Analyser
A method to automatically select the significant harmonics in spectra measured with the Nonlinear Vectorial Network Analyser is designed. This stochastic selection criterion is based on the -distribution. The method allows to save and calibrate all and only significant spectral components, without the need for wild guessing the number of significant harmonics in the measured data. In the following figure an example is shown. The harmonics selected through the stochastic selection criterion are pin-pointed with a alfa-sign. The right upper corner of each plot shows a zoom of the significant harmonics.
Spectral component
Modelling in the Presence Of Switching Uncertainties
A method that takes into account the switching uncertainties in the signal path of the measurement devices during the identification of device models is developed. Switching phenomena in the measurement instrument, like switching attenuators, induce jumps in the measured input-output characteristic that can be much larger than the noise contributions. The proposed method eliminates the subsequent model error by considering these jumps as a signal path state dependent stochastic contribution.
Measurement Based Nonlinear Modelling of Spectral Regrowth
The ability of Nonlinear models extracted from measured Continuous Wave (CW) amplifier data to predict the amplifier response to modulated signals in compression is investigated. Experimental verification of the Nonlinear model prediction is hereby performed using Nonlinear Vectorial Network Analyser (NVNA) measurements
New measurement and modelling techniques based on the Nonlinear Vectorial Network Analyser
Wendy Van Moer
An Automatic Harmonic Selection Scheme for Measurements and Calibration with the Nonlinear Vectorial Network Analyser
A method to automatically select the significant harmonics in spectra measured with the Nonlinear Vectorial Network Analyser is designed. This stochastic selection criterion is based on the -distribution. The method allows to save and calibrate all and only significant spectral components, without the need for wild guessing the number of significant harmonics in the measured data. In the following figure an example is shown. The harmonics selected through the stochastic selection criterion are pin-pointed with a alfa-sign. The right upper corner of each plot shows a zoom of the significant harmonics.
Spectral component
Modelling in the Presence Of Switching Uncertainties
A method that takes into account the switching uncertainties in the signal path of the measurement devices during the identification of device models is developed. Switching phenomena in the measurement instrument, like switching attenuators, induce jumps in the measured input-output characteristic that can be much larger than the noise contributions. The proposed method eliminates the subsequent model error by considering these jumps as a signal path state dependent stochastic contribution.
Measurement Based Nonlinear Modelling of Spectral Regrowth
The ability of Nonlinear models extracted from measured Continuous Wave (CW) amplifier data to predict the amplifier response to modulated signals in compression is investigated. Experimental verification of the Nonlinear model prediction is hereby performed using Nonlinear Vectorial Network Analyser (NVNA) measurements
| Acronym | IWT14 |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/98 → 31/12/01 |
Keywords
- non-linear
- Black box modelling
- microwaves
Flemish discipline codes in use since 2023
- Computer engineering, information technology and mathematical engineering
- Electrical and electronic engineering
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