Split augmented hammerstein model with neural network for a three-stage doherty power amplifier

Authors

  • Mun-Woo Lee,

    1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk 790-784, Republic of Korea
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  • Sang-Ho Kam,

    1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk 790-784, Republic of Korea
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  • Yoon-Ha Jeong

    Corresponding author
    1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk 790-784, Republic of Korea
    2. Division of IT Convergence Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk 790-784, Republic of Korea
    • Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk 790-784, Republic of Korea
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Abstract

This article describes a split augmented Hammerstein (SAH) model with a neural network (NN) for the three-stage Doherty power amplifier (DPA). The NN in the memoryless system is used to characterize the severe nonlinear changes in the magnitude and phase distortions. Several finite impulse response filters in the memory system are used to represent the memory effects of the DPA. The three-stage DPA implemented by Si LDMOSFETs is used to validate the performance of the proposed model. For a 2-FA wideband code division multiple access (WCDMA) signal with 10-MHz carrier spacing, the experimental results show that the proposed model characterizes not only the inconsistent changes in the static nonlinearity, but also the memory effects of the three-stage DPA. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:124–126, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26517

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