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Neural Network Modeling of Memory Effects in RF Power Amplifier Using Two-tone Input Signals  

Hwangbo Hoon (School of Information and Communication Engineering, Sungkyunkwan University)
Kim Won-Ho (School of Information and Communication Engineering, Sungkyunkwan University)
Nah Wansoo (School of Information and Communication Engineering, Sungkyunkwan University)
Kim Byung-Sung (School of Information and Communication Engineering, Sungkyunkwan University)
Park Cheonsuk (School of Information and Communication Engineering, Sungkyunkwan University)
Yang Youngoo (School of Information and Communication Engineering, Sungkyunkwan University)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.16, no.10, 2005 , pp. 1010-1019 More about this Journal
Abstract
In this paper, we used neural network technique to model memory effects of RF power amplifier which is fed by two-tone input signals. The memory effects in power amplifier were identified by observing the unsymmetrical distribution of IMD(Inter-Modulation Distortion) measurements with the change of tone spacings and power levels. Different asymmetries of IMD were also found at different center frequencies. We applied TDNN technique to model LDMOS power amplifier based on two tone IMD data, and the accuracy was very high compared to other modeling methods such as the(memoryless) adaptive modeling method.
Keywords
RF Power Amplifier; Memory Effect; Tapped Delay Line-Neural Network(TDNN);
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