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一种最小二乘/奇异值分解算法 被引量:10

Least Square/Singular Value Decomposition Algorithm
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摘要 针对预失真技术中存在记忆非线性放大器预失真的问题,分析数字预失真器的结构和常用预失真器的识别算法,对经典最小二乘/奇异值分解(LS/SVD)算法进行改进,以较少资源获得较高性能。仿真结果表明,改进的LS/SVD算法能实现记忆非线性放大器的快速、高效线性化,提高记忆非线性放大器的性能。 Aiming at the problem of predistortion for memory nonlinear amplifier in predistortion technology, this paper analyzes the structure of digital predistortion and recognization algorithm of common predistortion, improves classical Least Square/Singular Value Decomposition (LS/SVD) algorithm. Improved LS/SVD algorithm can obtain better performance by less resource. Simulation results show the proposed algorithm can realize fast and effective linearization of memory nonlinear amplifier, and improve its performance.
出处 《计算机工程》 CAS CSCD 北大核心 2009年第16期278-279,282,共3页 Computer Engineering
关键词 数字预失真 HAMMERSTEIN模型 最小二乘 奇异值分解 digital predistortion Hammerstein model Least Square(LS) Singular Value Decomposition(SVD)
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参考文献2

  • 1Kiln J, Konstantinou K. Digital Predistortion of Wideband Signals Based on Power Amplifier Model with Memory[J]. Electronics Letters, 2001, 37(23): 1417-1418.
  • 2Bai Erwei. An Optimal Two Stage Identification Algorithm for Hammerstein-wiener Nonlinear Systems[C]//Proceedings of American Control Conference. Philadelphia, USA: [s. n.], 1998: 2756- 2760.

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