An Improved Direct Learning Architecture for Satellite High Power Amplifiers Pre-Distortion

Mao Qiang Yang; Dao Xing Guo; Xiao Fei Pan; Lu Lu; Long Yang

doi:10.4028/www.scientific.net/AMM.340.1020

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 340An Improved Direct Learning Architecture for...

An Improved Direct Learning Architecture for Satellite High Power Amplifiers Pre-Distortion

Abstract:

The pre-distortion techniques based on traditional direct learning architecture (DLA) are commonly used to linearize high power amplifiers (HPAs) settled in ground-station or on board satellite merely, which cannot achieve a satisfying linearization. In this paper, an improved DLA, considering the satellite channel features with two HPAs in the loop, is put forward. Nonlinear polynomial with 7 order and 4 memory depth is utilized as pre-distorter and recursive least square (RLS) algorithm is adopted for its parameters updating. Simulation results show that with pre-distortion the power spectrum density (PSD) of 16APSK signal achieves about 7.2dB improvement, the EVM of signal constellation is greatly improved up to 61.83%, and desirable system linearization performance is acquired.

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Periodical:

Applied Mechanics and Materials (Volume 340)

Pages:

1020-1024

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.340.1020

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Online since:

July 2013

Authors:

Mao Qiang Yang, Dao Xing Guo, Xiao Fei Pan, Lu Lu, Long Yang

Keywords:

Digital Pre-Distortion, Direct Learning Architecture (DLA), High Power Amplifiers, Linearization, Satellite Communication

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