用户名: 密码: 验证码:
射频功率放大器大信号表征及频域非线性特征建模
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
射频/微波系统中的功率放大器作为通信系统中的核心元器件,放大器产生的非线性现象是影响射频/微波通信系统性能的重要因素,得到专家和学者们的普遍关注。人们对射频功率器件提出了更多的新要求,如更高的功率,更高效率和更小的体积。然而,传统的设计射频功率器件的逐次渐近法非常的耗时,而且需要有经验积累的过程,不利于快速地发布产品。在这种情况下,业界专家们把目光转向了更具发展潜力的新技术上,这就是计算机辅助设计技术。
     非线性功率器件应用计算机辅助设计技术的基础是要求建立足够精确的非线性大信号射频模型。如果这些模型存在的话,就可以减少大量的重复性工作,使得设计工作更加简便和快捷。大信号网络分析仪的出现和大信号散射函数概念的提出,让射频和微波领域的工程师开始以一种全新的视角来审视现有建模方法所存在的各种问题。
     本文首先介绍了现有的射频功率器件建模方法,指出了这些方法的局限性,提出了一种基于支持向量机的射频功率放大器建模方法,通过辨识大信号散射函数,精确的表征射频功率放大器的非线性特性。实验结果证实了这种方法的有效性,通过与国外基于神经网络的方法对比,在模型的精度相当的情况下,这种方法具有所需的数据量少,因而相应地使测试的时间减少的优点。这为发展射频微波功率器件的设计方法探索了一个新的途径。
     基于大信号网络分析仪测量数据,本文设计了针对射频功率放大器的建模实验,提出了一种对散射函数一阶近似的射频功率放大器建模方法,并用压缩特性、大信号反射系数、AM-PM特性、谐波失真特性等多种应用实例验证了模型的有效性。
     为了便于对射频功率器件进行集成研究,本文开发了一个射频功率放大器大信号表征及频域非线性特性建模平台,这个平台可以实现文中提出的两种建模方法:第一个模块可以实现散射函数的辨识,并通过辨识得到的散射函数分析射频功率放大器的压缩特性、AM-PM特性、谐波失真特性、时域电压电流。第二个功能可以实现:通过实测得到的射频功率放大器的频域数据,计算得到多谐波失真模型(Polyharmonic Distortion Modeling,PHD model),通过PHD模型分析其与经典S参数模型的区别,并分析功率放大器的压缩特性、AM-PM特性、大信号反射系数等非线性特性。
     射频功率放大器大信号表征及频域非线性特性建模是当前的研究热点,各国学者都在不同方向上进行着探索,随着研究工作的深入,相信它的理论意义和实用价值会被更多的学者认识到。
Microwave power amplifier has become the dominant device as the nonlinear distortion generated by amplifier plays a key role in influencing the efficiency of transmitter/receiver. This dominance has resulted in more requirements for RF power components, for example, higher power and efficiency of transmitter/receiver and smaller in size. However, Empirical cut-and-try methods in wireless infrastructure power devices design are time-consuming and require an experienced touch. As a result, experts turn to or even become fascinated with one rapidly developing technology, Computer-aid Design.
     Fundamental to the Computer-aid Design for nonlinear devices is building adequate nonlinear large-signal RF models. If these models exist, nonlinear devices design and simulation can save numerous iterations and simplify design.
     The introduction of the notion of large signal scatter functions for modeling nonlinear RF devices exploits a novel way for researchers to solve the existing problems of modeling from new perspectives.
     The thesis begins with an introduction of present methods of modeling RF power component as well as their advantages and limitations, and then proposes a new approach to modeling RF power component based on SVMs by identifying large signal scatter functions and accurately characterizing the nonlinear properties of RF power component. Findings prove the effectiveness of the approach and the characteristic of requiring fewer data compared with the existing ANN-based method and the resulted advantage of less test time.
     Based on nonlinear large-signal network analyzer measurement data, we describe a novel method to analyze RF modeling and testify its effectiveness with various experiments using AM-AM, AM-PM, harmonic distortion analysis and so on.
     We design a RF power amplifier behavior modeling software platform to realize two purposes: one is to identify scattering functions and then to acquire AM-AM, AM-PM, harmonic distortion characters and time domain current and voltage waveforms; the other is to calculate PHD model by using measurement data and to analyze nonlinear characters of power amplifier such as AM-AM, AM-PM, harmonic distortion characters and Large-signal reflection coefficient by comparing the PHD model with classical S parameter model.
     The research of RF large signal behavior and frequency modeling is a hot issue at present and many scholars all over the world are devoted themselves to exploring it in different directions. With further research and experiments, we are sure that more researchers will discover its advantages and values.
引文
1郑博仁.射频/微波放大器非线性特性分析方法比较.信息与电子工程. 2005,3(3):201-206
    2 M. Schetzen. The Volterra and Wiener Theories of Nonlinear Systems. New York: John Wiley & Sons. 1980
    3 L. O. Chua, and C-Y. Ng. Frequency-Domain Analysis of Nonlinear Systems: Formulation of Transfer Functions. IEE J. Electronic Circuits & Systems. 1979,3:257-269
    4 S. A. Mass. Third-Order Intermodulation Distortion in Cascaded Stages. IEEE Microwave & Guided Wave Letters. 1995,5(6):189-191
    5 Jose Carlos Pedro. Intermodulation Distortion in Microwave and Wireless Circuits. Artech House. 2003
    6 Qiang Wu, Martina Testa, Robert Larkin. On Design of Linear RF Power Amplifier for CDMA Signals. RF Microwave Computer-Aided Eng. 1998, 8(3):283-292
    7 Q. J. Zhang, K. C. Gupta. Neural Networks for RF and Microwave Design. Norwood, MA:Artech House. 2000
    8 K. C. Gupta. Emerging Trends in Millimeter-Wave CAD. IEEE Trans. on Microwave Theory and Techniques. 1998,46:747-755
    9 G. L. Creech, B. J. Paul, C. D. Lesniak, T. J. Jenkins, and M. C. Calcatera. Artificial Neural Networks for Fast and Accurate EM-CAD of Microwave Circuits. IEEE Trans. on Microwave Theory and Techniques. 1997, 45(5):794-802
    10 V. K. Devabhaktuni, M. C. E. Yagoub and Q. J. Zhang. A Robust Algorithm for Automatic Development of Neural Network Models for Microwave Applications. IEEE Trans. Microwave Theory Tech. 2001,49:2282-2291
    11 F. Wang, Q. J. Zhang. Knowledge Based Neural Models for Microwave Design. IEEE Trans. on Microwave Theory and Techniques. 1997,45:2333-2343
    12 F. Wang, V. Devabhaktuni and Q. J. Zhang. A Hierarchical Neural Network Approach to the Development of Library of Neural Models for MicrowaveDesign. IEEE Trans. Microwave Theory and Techniques. 1998,46:2391-2403
    13 P. M. Watson, K. C. Gupta. EM-ANN Models for Microstrip Vias and Interconnects in Dataset Circuits. IEEE Trans. on Microwave Theory and Techniques. 1996,44(12):2495-2503
    14 J. W. Bandler, M. A. Ismail, J. E. R. Sanchez and Q. J. Zhang. Neuromodeling of Microwave Circuits Exploiting Space Mapping Technolgy. IEEE Trans. on Microwave Theory and Techniques. 1999,47:2417-2427
    15 P. M. Watson, K. C. Gupta. Design and Optimization of CPW Circuits Using EM-ANN Models for CPW Components. IEEE Trans. on Microwave Theory and Techniques. 1997,45:2515-2523
    16 G. L. Creech, J. P. Bradley, C. D. Lesniak, T. J. Jenkins, M. C. Calcatera. Artificial Neural Networks for Fast and. Accurate EM-CAD of Microwave Circuits. IEEE Trans. on Microwave Theory and Techniques. 1997, 45(5):794-802
    17 A. H. Zaabab, Zhang Q. J, M. Nakhla, A Neural Network Modeling Approach to Circuits Optimization and Statistical Design. IEEE Trans. on Microwave Theory and Techniques. 1995,43:1349-1358
    18 Jianjun Gao, Lei Zhang, Jianjun Xu, Q-J Zhang. Nonlinear HEMT Modeling Using Artificial Neural Network Technique. IEEE International Microwave Symposium Digest. 2005:469-472
    19 Xiuping Li, Jianjun Gao. PHEMT Modeling by Using Neural Network Technique. IOP Semiconductor. Science Technology. 2006,21(5):833–840
    20 J. de Villiers, E. Barnard. Backpropagation Neural Nets with One and Two Hidden Layers. IEEE Trans. on Neural Networks. 1993,4:136-141
    21 Y. Fang, M. Yagoub, F. Wang, and Q. J. Zhang. A New Macromodeling Approach for Nonlinear Microwave Circuits Based on Recurrent Neural Networks. IEEE Trans. on Microwave Theory and Techniques. 2000,48:2334-2344
    22 M. Vai, S. Prasad. Microwave Circuit Analysis and Design by a Massively Distributed Computing Network. IEEE Trans. on Microwave Theory and Techniques. 1995,43:1087-1094
    23 J. J. Xu, M. C. E. Yagoub, R. Ding and Q. J. Zhang. Neural-based Dynamic Modeling of Nonlinear Microwave Circuits. IEEE Trans. on MicrowaveTheory and Techniques. 2002,50:2769-2780
    24 G. Thimm, E. Fiesler. High-order and Multilayen Perceptron Initialization. IEEE Trans. on Neural Networks. 1997,8(3):349-359
    25 A. Veluswami, M. S. Nakhla and Q. J. Zhang. The Application of Neural Networks to EM-based Simulation and Optimization of Interconnects in High-speed VLSI Circuits. IEEE Trans. on Microwave Theory and Techniques. 1997,45:712-723
    26 M. A. Khatibzadeh and R. J. Trew. A Large-signal Analytical Model for the GaAs MESFET. IEEE Trans. on Microwave Theory and Techniques. 1990,38:1480-1487
    27 R. Griffith and M. S. Nakhla. Time-domain Analysis of Lossy Coupled Transmission Lines. IEEE Trans. on Microwave Theory and Techniques. 1990,38(10):1480-1486
    28 M. Vai, S. Wu, B. Li and S. Prasad. Reverse Modeling of Microwave Circuits with Bidirectional Neural Network Models. IEEE Trans. on Microwave Theory and Techniques. 1998,46:1492-1494
    29 D. E. Rumelhart, G. E. Hinton and R. J. Williams. Learning Internal Representations by Error Propagation. Parallel Distributed Processing. 1986,1:318-362
    30 P. M. Watson, C. Cho and K. C. Gupta. Electromagnetic-Artificial Neural Network Model for Synthesis of Physical Dimensions for Multilayer Asymmetric Coupled Transmission Structures. International Journal of RF and Microwave Computer-Aided Engineering, Special Issue on Applications of ANN to RF and Microwave Design. 1999,9:175-186
    31 K. Hornik, M. Stinchombe, H. White. Multilayer Feedforward Networks are Universal Approximators. Neural Networks. 1989,2(2):359-366
    32 T. Y. Kwok, D. Y. Yeung. Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Trans. on Neural Networks. 1997,8:630-645
    33 J. A. Jargon, K. C. Gupta and D. C. DeGroot. Application of Artificial Neural Networks to RF and Microwave Measurements. International Journal of RF and Microwave Computer-Aided Engineering. 2002,12:3-24
    34王卓鹏,杨卫平.快速模拟退火算法用于MESFET大信号建模.系统工程与电子学. 1999,21(11):56-57
    35高学邦,蒋敬旗,高建军. GaAsFET大信号建模和参数提取.半导体情报. 2000,37(4):44-51
    36王静,邓先灿. GaAs MESFET准二维动态大信号建模.电子学报. 2000,28(8):143-144
    37杨林安,于春利,张义门,张玉明. SiC MESFET的大信号电容解析模型.电子学报. 2002,32(2):229-231
    38曾天志,张波,罗萍,蒲奎.一种新颖的功率MOSFET SPICE宏模型.微电子学. 2006,36(4):407-410
    39墙威,曹阳,鄢媛媛,高洵.基于BP神经网络的CMOS电路平均功耗宏模型.武汉大学学报(理学版). 2006,52(3):353-356
    40胡辉勇,张鹤鸣,吕懿,戴显英. SiGe HBT大信号等效电路模型.物理学报. 2006,55(1):403-408
    41翟小社,王建华,宋政湘,耿英三.信号完整性分析中时域宏模型结合电路仿真的方法研究.西安交通大学学报. 2007,41(6):631-635
    42 Wendy Van Moer and Yves Rolain. A Large-Signal Network Analyzer: Why Is It Needed?. IEEE Microwave Magazine. 2006,7(6):46-62
    43 M. Sipila, K. Lehtinen, and V. Porra. High-frequency Periodic Time-domain Waveform Measurement System. IEEE Trans. on Microwave Theory and Techniques. 1988,36(10):1397-1405
    44 Urs Lott. Measurement of Magnitude and Phase of Harmonics Generated in Nonlinear Microwave Two-ports. IEEE Trans. on Microwave Theory and Techniques. 1989,37(10):1506-1511
    45 G. Kompa, F. van Raay. Error-corrected Large-signal Waveform Measurement System Combining Network Analyser and Sampling Oscilloscope Capabilities. IEEE Trans. on Microwave Theory and Techniques. 1990,38(4):358-365
    46 M. Demmler, P. J. Tasker and M. Schlechtweg. A Vector Corrected High Power On-wafer Measurement System with a Frequency Range for the Higher Harmonics up to 40 GHz. Proceedings of the European Microwave Conference. 1994:1367–1372
    47林茂六,徐洪光.射频微波网络非线性测量理论与技术的新发展.中国电子学会第六届全国学术会议. 2001:3-4
    48 J. Verspecht. The Return of the Sampling Frequency Convertor, 62nd ARFTG Conference Digest. 2003:2-11
    49 J. Verspecht, Calibration of a Measurement System for High Frequency Nonlinear Devices. Doctoral Dissertation at the Vrije Universiteit Brussel, Brussels, Belgium. 1995:26-31
    50 C. William. High-power Microwave Generation Using Optically Activate Semiconductor Switches. IEEE Trans. on Electron Devices. 1990: 2439-2448
    51 C. H. LEE. Picosecond Optics and Microwave Technology. IEEE Trans. on Microwave Theory and Techniques. 1990:596-607
    52王海龙,吴群,林茂六.射频取样下变频器本振脉冲产生电路设计. 2005全国微波毫米波会议论文集. 2006:1083-1086
    53林茂六,吴群,王海龙等.宽带RF-IF采样下变频器采样频率特性的研究,微波学报增刊. 2005,21:112-117
    54 Hailong Wang, Qun Wu,Maoliu Lin. Research on the Sampling Frequency for Broad Band RF-IF Sampling Down-Converters. China-Japan Joint Meeting on Microwaves. 2004:57-60
    55 J. S. Lee, C. Nguyen. Uniplanar Picoseconds Pulse Generator Using Step-recovery Diode. ELECTRONICS LETTERS. 2001,37(8):504-506
    56 J. S. Lee, C. Nguyen. A Low-Cost Uniplanar Sampling Down-Converter with Internal Local Oscillator, Pulse Generator, and IF Amplifier. IEEE Trans. on Microwave Theory and Techniques. 2001,49(2):390-392
    57 J. Verspecht. Broadband Sampling Oscilloscope Characterization with The‘nose-to-nose’Calibration Procedure: A Theoretical and Practical Analysis. IEEE Transactions on Instrumentation and Measurement. 1995,44(6):991-997
    58 J. Verspecht, K. Rush. Individual Characterization of Broad-band Sampling Oscilloscopes with a Nose-to-nose Calibration Procedure. IEEE Transactions on Instrumentation and Measurement. 1994,43(2):347-354
    59 J. Verspecht. Quantifying the Maximum Phase-distortion Error Introduced by Signal Samplers. 1997,46(3):660-666
    60 T. V. den Broeck, J. Verspecht. Calibrated Vectorial Nonlinearnetwork Analyzers. IEEE MTT-S International Microwave Symposium Digest. 1994: 1069-1072
    61 K. A. Remley, D. F. Williams, D. C. DeGroot, et al. Effects of Nonlinear Diode Junction Capacitance on the Nose-to-nose Calibration. IEEE Microwave and Wireless Components Letters. 2001,11(5):196-198
    62 J. Verspecht, D. Schreurs and B. Nauwelears. Black Box Modeling of Hard Nonlinear Behavior in the Frequency Domain. IEEE MTT-S International Microwave Symposium Digest. 1996:1734-1738
    63 J. Verspecht, Dominique Schreurs. Recent Advances in the Measurement and Black-Box Modelling of High-Frequency Components. GAAS99 Conference Proceedings. 1999:387-392
    64 D. Schreurs, J. Verspecht, B. Nauwelaers and A. Van De Capelle. Direct Extraction of Non-linear Model from Vectorial Large-signal Measurements. International Workshop on Advanced Blackbox for Non-linear Modeling. 1998:228-233
    65 J. Verspecht, P. Van Esch. Accurately Characterizing Hard Nonlinear Behavior of Microwave Components with the Nonlinear Network Measurement System: Introducing Nonlinear Scattering Functions. Proceedings of the 5th International Workshop on Integrated Nonlinear Microwave and Millimeterwave Circuits. 1998:17-26
    66 J. Verspecht. Scattering Functions for Nonlinear Behavioral Modeling in the Frequency Domain. 2003 IEEE MTT-S Int. Microwave Symp. Workshop. 2003
    67 V. N. Vapnik. The Nature of Statistical Learning Theory, New York: Springer, 1999
    68 V. N. Vapnik. Statistical Learning Theory, New York: Wiley, 1998
    69 V. N. Vapnik. An Overview of Statistical Learning Theory, IEEE Trans. on Neural Network. 1999,10(5):988-999
    70 B. E. Boser, I. M. Guyon, V. N. Vapnik. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the 5th Annual ACM Workshop on Computation Learning Theory. 1992:144-152
    71 Dong Seong Kim, Ha Nam Nguyen, Jong Sou Park. Genetic Algorithm to Improve SVM Based Network Instrusion Detection System. Proceedings of AINA. 2005:154-158
    72 E. Osuna, R. Freund, F. Girmi. An Improved Training Algorithm for SupportVector Machines. Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing. 1997:276-285
    73 C. W. Hsu, C. J. Lin. A Simple Decomposition Method for Support Vector Machines. Machine Learning. 2002,46(1):291-314
    74 K. Hotta. Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. Proceedings of the 10th European Conference on Machine Learning. 1998:137-142
    75 J. C. Platt. Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999:184-208.
    76 S. S. Keerthi, S. K. Shevade, C. Bhattacharyya. Improvement to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 2001,13(3):637-649
    77 J. C. Platt. Using Analytic QP and Sparseness to Speed Training Support Vector Machines. Advances in Kernel Methods:Support Vector Learning. 1999:185-208
    78 X. D. Jian, Y. S. Ching and K. A. Adam. Fast SVM Training Algorithm. International Journal of Pattern Recognition and Artificial Intelligence. 2003, 17(3): 367-384
    79 J. A. K. Suykens, J. Vandewalle. Least Squares Support Vector Machine Classifiers. Neural Processing Letters. 1999,9(3):293-300
    80 S. S. Keerthi, S. K. Shevade. SMO Algorithm for Least-Squares SVM Formulations. Neural Computation. 2003,15(2):487-507
    81 N. Syed, H. Liu and K. Sung. Incremental Learning with Support Vector Machines. Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Articial Intelligence. 1999:352-356
    82 P. Mitra, C. A. Murthy, S. K. Pal. Data Condensation in Large Databases by Incremental Learning with Support Vector Machines. Proc. Internat. Conf. Pattern Recognition (ICPR2000). 2000:712-715
    83 D. Mattera, F. Palmieri, S. Haykin. An Explicit Algorithm for Training Support Vector Machines. IEEE Signal Processing Letters. 1999,6(9):243-244
    84 G. Cauwenberghs, T. Poggio. Incremental and Decremental Support VectorMachine. Advances in Neural Information Processing Systems. 2001,13: 409-415
    85 G. Fung , O. Mangasarian. Incremental Support Vector Machine Classification. Tech. Rep. 01-08, Data Mining Institute, Computer Sciences Department, University of Wisconsin, 2001:1-14
    86 M. H. Yang, N. Ahuja. Geometric Approach to Train Support Vector Machines. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2000:430-437
    87 Chih-Chung Chang and Chih-Jen Lin, LIBSVM: A Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
    88林茂六,孙洪剑,姜靖,吴群.消除相位模糊的谐波相关复信号比值计算方法.电子学报. 2006,34(6):1130-1133.
    89 D. E. Root, J. Verspecht, D. Sharrit, J. Wood, and A. Cognata. Broad-band Poly-harmonic Distortion (PHD) Behavioral Models from Fast Automated Simulations and Large-signal Vectorial Network Measurements. IEEE Trans. on Microwave Theory and Techniques. 2005,53(11):3656-3664
    90 J. Verspecht, D. E. Root, J. Wood, et al. Broad-band, Multi-harmonic Frequency Domain Behavioral Models from Automated Large-signal Vectorial Network Measurements, IEEE MTT-S Microwave Symposium Digest, 2005:12-17
    91 J. Verspecht, D. E. Root. Polyharmonic Distortion Modeling. IEEE Microwave Magazine. 2006,7(3):44-57
    92 J. Verspecht. Hot S-parameter Techniques: 6 = 4 + 2. ARFTG Microwave Measurements Conf. Dig. 2005:7-15
    93 Hot S22 and Hot K-factor Measurements-Practical S-parameter Measurements for Power Amplifier Applications. Anritsu Application note, August 2002
    94 Joel Dunsmore and Wayne Smith. Predicting Out-of-band Nonlinear Power Amplifier Stability Using Hot S-parameters. Microwave Engineering Europe, 2005:23-28
    95 Large-Signal S-parameter Simulation. Advanced Design System (ADS) Manual. Agilent Technologies. September 2004
    96 Tony Gasseling, Denis Barataud, Sébastien Mons, Jean-Michel Nébus, Jean-Pierre Villotte, Juan J. Obregon and Raymond Quéré. Hot Small-signal S-parameter Measurements of Power Transistors Operating Under Large-signal Conditions in a Load-Pull Environment for the Study of Nonlinear Parametric Interactions. IEEE Transactions on Microwave Theory and Techniques. 2004,52(3):805-812
    97 Arnaud Soury, Edouard Ngoya, Jean Rousset. Behavioral Modeling of RF and Microwave Circuit Blocs for Hierarchical Simulation of Modern Transceivers. Conference Record of the 2005 IEEE MTT-S International Microwave Symposium. 2005:975-978
    98 J. Verspecht. Everything You’ve Always Wanted to Know about Hot-S22 (but Were Afraid to Ask). IMS 2002 Workshop“Introducing New Concepts in Nonlinear Network Design. 2002
    99 J. D. Martens, P. Kapetanic. Probe-tone S-parameter Measurements. IEEE Trans. on Microwave Theory and Techniques. 2002,50:2076-2082
    100 M. L. Edwards, J. H. Sinsky. A New Criterion for Linear 2-port Stability using Geometrically Derived Parameters. IEEE Trans. on Microwave Theory and Techniques. 1992,40(12):2303-2311
    101 J. M. Rollet. Stability and Power Gain Invariants of Linear Two-ports. IRE Transactions on Circuit Theory. 1962,CT-9:29-32

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700