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Digital Predistortion Using Adaptive Basis Functions
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AI
DOI：10.1109/TCSI.2013.2265958.png)
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This paper is concerned with digital predistortion (DPD) for linearization of radio frequency (RF) high power amplifiers (PAs). We propose an adaptive scheme for selecting basis functions for both direct and indirect learning digital predistortion architectures. The adaptive scheme has the advantage of reducing the complexity and, at the same time, increasing the stability of digital predistortion. Simulation and lab experimental results are presented to demonstrate the effectiveness of using adaptive basis functions in a hardware platform with a solid state high power amplifier.
Keyword:
Adaptive selection of basis functions
digital predistortion
direct learning
indirect learning
power amplifier linearization
predistorter
stability of digital predistortion
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5.2
论文数:
9.7K
被引数:
2.2W
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