Exploring predistortion training algorithms in a Cartesian feedback-trained digital predistortion system for RF power amplifier linearization
Author(s)
Huang, Jeffrey B
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Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Joel L. Dawson.
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A Cartesian feedback-trained digital predistortion system for RF power amplifier linearization offers many advantages with its combination of two different linearization techniques. This thesis describes such a system, focusing on the important issue of predistorter training. It examines and analyzes in great detail the promising loop filter pre-charging optimization and the tradeoffs associated with such training, developing a model that provides many valuable system design insights. In order establish a means to experimentally verify the theory and explore predistortion training algorithms, this thesis presents the design, development, and characterization of a mock-up prototype that models the essential features of the actual Cartesian feedback-trained digital predistortion system. The mock-up serves as a standalone proof-of-concept system that demonstrates the benefits and tradeoffs of loop filter pre-charging in predistorter training. It confirms the theory while also revealing practical issues pertaining to the limits on performance.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (p. 117-118).
Date issued
2006Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.