Abstract
The deficiency of predistortion performance exists in indirect NN (Neural Network)-predistorter learning methods for nonlinear high power amplifiers (HPAs), and direct NN-predistorter learning methods possess great computational complexity. To circumvent these problems, in this paper we propose a novel NN-predistorter learning method with its structure developed by using some properties of nonlinear operators and its corresponding algorithm derived by using an approximation formula. The proposed method is based on the identification of NN post-distorter of the HPA, and then directly implements the efficient Levenberg-Marquardt back propagation algorithm. Thus, compared with the direct NN-predistorter learning method, our proposed method reduces the computational complexity and still keeps slightly better predistortion performance. Theoretical analysis and simulation results also show our proposed method outperforms the indirect NN-predistorter learning method in the term of about 5 dB adjacent channel power ratio improvement.
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The Project was supported by the National Innovation Fund (No. 06026225101735) and National Nature Science Foundation of China (No. 60901004) and Special Fund for Basic Scientific Research of Central Colleges of China for Chang’an University (CHD2009JC098) and Special Fund for Basic Research support programs of Chang’an University and Shaanxi Engineering and Technique Research Center for Road and Traffic Detection.
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Cui, H., Zhao, XM. A Novel NN-Predistorter Learning Method for Nonlinear HPA. Wireless Pers Commun 63, 469–482 (2012). https://doi.org/10.1007/s11277-010-0144-z
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DOI: https://doi.org/10.1007/s11277-010-0144-z