This thesis presents a study and implementation of several measurement procedures used to efficiently generate non-linear measurement-based behavioral models primary for microwave amplifiers. Behavioral models are a solution for representing devices that can present linear and/or non-linear behavior when little or no information about the internal structure is known. Measurement-based behavioral models are an advantage since they can be extracted from a direct measurement of the device. This work addresses some of the challenges of these types of measurements. A set of software modules has been produced that combine several modern techniques to efficiently generate practical models using equipment commonly available in a typical microwave lab. Advanced models using new and more complex equipment are also discussed.
Modeling of the non-linear operation of power amplifiers is a common subject of study since it provides a path to improved system simulations. However, the measurement process used for non-linear behavioral modeling of PAs requires either non-linear measurement instrumentation, not yet widely available, or numerous measurements that makes the process tedious and susceptible to errors. Power dependent S-Parameters obtained with a conventional Vector Network Analyzers (VNA) can be used to extract AM-to-AM and AM-to-PM behavior of a device and to generate, simple but useful, behavioral models. A careful analysis of the characteristics of common RF measurement instrumentation combined with knowledge of common non-linear phenomena provides with the conditions under which useful models can be generated.
The results of this work are presented as several programs implemented in National Instruments LabVIEW that will sequence through the different measurements required for the generation of measurement-based behavioral models. The implemented models are known as P2D and S2D models available with Agilent Advanced Design System (ADS.) The code will communicate with the measurement instrumentation and decide on the most efficient way to extract the data. Once the data is extracted, the code will put into the appropriate syntaxes required by the model for direct and convenient setup of the generated models in ADS.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-1029 |
| Date | 18 June 2009 |
| Creators | Sosa Martin, Daniel |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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