Performance of a recursive algorithm for polynomial predistorter design

Acta Metallurgica Sinica(English letters) ›› 2008, Vol. 15 ›› Issue (3): 95-99.doi:

Performance of a recursive algorithm for polynomial predistorter design

许灵军吴晓光王勇张平

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2007-09-08 修回日期:1900-01-01 出版日期:2008-09-30
通讯作者: 许灵军

Performance of a recursive algorithm for polynomial predistorter design

XU Ling-jun, WU Xiao-guang, WANG Yong, ZHANG Ping

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2007-09-08 Revised:1900-01-01 Online:2008-09-30

摘要/Abstract

摘要：

In this article, based on least square estimation, a recursive algorithm for indirect learning structure predistorter is introduced. Simulation results show that of all polynomial predistorter nonlinear terms, higher-order (higher than 7th-order) nonlinear terms are so minor that they can be omitted in practical predistorter design. So, it is unnecessary to construct predistorter with higher-order polynomials, and the algorithm will always be stable. Further results show that even when 15th-order polynomial model is used, the algorithm is convergent after 10 iterations, and it can improve out-band spectrum of 20 MHz bandwidth signal by 64 dB, with a 1.2×1011 matrix condition number.

关键词:

digital;predistortion,;recursive;algorithm,;power;amplifier;linearization,;polynomial;predistorter

Abstract:

Key words:

digital predistortion;recursive algorithm;power amplifier linearization;polynomial predistorter

XU Ling-jun, WU Xiao-guang, WANG Yong, ZHANG Ping. Performance of a recursive algorithm for polynomial predistorter design[J]. Acta Metallurgica Sinica(English letters), 2008, 15(3): 95-99.

参考文献

1. Faulkner M, Johansson M. Adaptative linearization using predistortion: experimental results. IEEE Transactions on Vehicular Technology, 1994, 43(2): 323-332

2. Mingo J D, Valdovinos A. Performance of a new digital baseband predistorter using calibration memory. IEEE Transactions on Vehicular Technology, 2001, 50(4): 1169-1176

3. Teikari I, Vankka J, Halonen K, et al. Baseband digital predistorter with quadrature error correction. Analog Integrated Circuits and Signal Processing, 2006, 46(1): 73-85

4. Xu L J, Wu X G , Wang Q, et al. Adaptive digital predistorter for radio frequency power amplifier linearization. Proceedings of IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies for Wireless Communications, Aug 14-17, 2007, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2007: 1390-1393

5. Lee K, Gardner P. Comparison of different adaptation algorithms for adaptive digital predistortion based on EDGE standard. Proceedings of IEEE International Microwave Symposium Digest: Vol 2, May 20-25, 2001, Phoenix, AZ, USA. Piscataway, NJ, USA: IEEE, 2001: 1353-1356

6. Faulkner M, Mattsson T. Spectral sensitivity of power amplifiers to quadrature modulator misalignment. IEEE Transactions on Vehicular Technology, 1992, 41(4): 516-525

7. Thai H V, Tho L N, Hichem B, et al. Adaptive polynomial predistorters for M-QAM transmission using nonlinear power amplifiers. Wireless Communications and Mobile Computing, 2007, 7(3): 267-283

8. Helaoui M, Boumaiza S, Ghazel A, et al. Power and efficiency enhancement of 3G multicarrier amplifiers using digital signal processing with experimental validation. IEEE Transactions on Microwave Theory and Techniques, 2006, 54(4): 1396-1403

9. Morgan D R, Ma Z, Kim J, et al. A Generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Transactions on Signal Processing, 2006, 54(10): 3852-3860

10. Ding L, Zhou G T, Morgan D R, et al. Memory polynomial predistorter based on the indirect learning architecture. Proceedings of IEEE Global Telecommunications Conference: Vol 1, Nov 17-21, 2002, Taipei, China. Piscataway, NJ, USA: IEEE, 2002: 967-971

11. Raich R, Qian H, Zhou G T, et al. Orthogonal polynomials for power amplifier modeling and predistorter design. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1468-1479

12. Eun C, Powers E J. A predistorter design for a memoryless nonlinearity preceded by a dynamic linear system. Proceedings of IEEE Global Telecommunications Conference, Nov 13-17, 1995, Singapore. Piscataway, NJ, USA: IEEE, 1995: 152-156

13. Ding L, Zhou G T. Effects of even-order nonlinear terms on power amplifier modeling and predistortion linearization. IEEE Transactions on Vehicular Technology, 2004, 53(1): 156-162

14. Raich R, Zhou G T. On the modeling of memory nonlinear effects of power amplifiers for communication applications. Proceedings of IEEE Digital Signal Processing Workshop, Oct 13-16, 2002, Pine Mountain, Georgia. Piscataway, NJ, USA: IEEE, 2002: 7-10

15. Zhu Q, Lu H. Pilot based channel estimation in IEEE 802.16a OFDM system. Journal of China Universities of Posts and Telecommunications, 2005, 12(1): 41-46

16. Saleh A. Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers. IEEE Transactions on Communications, 1981, 29(11): 1715-1720

[1]	朱帆, 杨娟. Fast TMRM: efficient multi-task recommendation model[J]. 中国邮电高校学报(英文版), 2020, 27(5): 13-22.
[2]	杨健健张强王晓林杜毅博王超吴淼. Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization [J]. 中国邮电高校学报(英文版), 2020, 27(4): 17-25.
[3]	周游段瑞枫蒋伯峰. Reduced-state noncoherent MLSD method for CPM in aeronautical telemetry [J]. 中国邮电高校学报(英文版), 2020, 27(4): 43-53.
[4]	李亚张丹青田心记 . Power allocation scheme for maximizing spectrum efficiency and energy efficiency tradeoff for multi-cluster NOMA system[J]. 中国邮电高校学报(英文版), 2020, 27(3): 62-72.
[5]	李文正贺鸣升. Imp Raft: a consensus algorithm based on Raft and storage compression consensus for IoT scenario[J]. 中国邮电高校学报(英文版), 2020, 27(3): 53-61.
[6]	费炜刘聪胡胜. Research on swarm intelligence optimization algorithm[J]. 中国邮电高校学报(英文版), 2020, 27(3): 1-20.
[7]	吴青高小凤范九伦张恒昌. Application of smoothing technique on twin support vector hypersphere[J]. 中国邮电高校学报(英文版), 2020, 27(3): 31-41.
[8]	Pan Ziyu, Hu Han. Energy efficiency optimization for HCNs based on small base station transmission power allocation[J]. 中国邮电高校学报(英文版), 2020, 27(2): 19-25.
[9]	王忠民田萌梁琛宋辉. Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach [J]. 中国邮电高校学报(英文版), 2020, 27(1): 81-91.
[10]	梁晓林曹旺斌. Broadband hybrid model for relay-aided cooperative MIMO power line communication systems[J]. 中国邮电高校学报(英文版), 2020, 27(1): 72-80.
[11]	Tian Zengshan, Ren Haoliang,Zhou Mu. Adaptive access points deployment for Indoor bluetooth positioning accuracy enhancement[J]. 中国邮电高校学报(英文版), 2019, 26(5): 82-93.
[12]	Qu Zhijian,Wang Shasha, Xu Hongbo, Li Panjing, Li Caihong. Self-adaptive mechanism based genetic algorithms for combinatorial optimization problems[J]. 中国邮电高校学报(英文版), 2019, 26(5): 11-21.
[13]	Peng Hong1, Ding Guangtai1,2, Zhang Huiran1,2,Hu Dongli2,3. Competition system for automatically evaluating edge detection algorithms based on the evolutionary game theory[J]. 中国邮电高校学报(英文版), 2019, 26(5): 49-59.
[14]	Deng Junyong1, Liu Yang1, Xie Xiaoyan2. Performance Characterization of Illumination Algorithms for Reconfigurable Graphics Processor[J]. 中国邮电高校学报(英文版), 2019, 26(5): 60-71.
[15]	秦彩王朝炜王卫东张英海. Dynamic power control for relay-aided transmission based on deep reinforcement learning[J]. 中国邮电高校学报(英文版), 2019, 26(3): 35-43.

Performance of a recursive algorithm for polynomial predistorter design

Performance of a recursive algorithm for polynomial predistorter design

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价