Steady-State Performance Analysis and Step-Size Selection for LMS-Adaptive Wideband Feedforward Power Amplifier Linearizer

被引:10
|
作者
Gokceoglu, Ahmet [1 ]
Ghadam, Ali Shahed Hagh [1 ]
Valkama, Mikko [1 ]
机构
[1] Tampere Univ Technol, Dept Commun Engn, FI-33101 Tampere, Finland
基金
芬兰科学院;
关键词
Feedforward linearizer; intermodulation distortion (IMD); LMS; power amplifier (PA); step-size; Wiener-Hammerstein;
D O I
10.1109/TSP.2011.2169254
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Balancing between power amplifier (PA) linearity and power efficiency is one of the biggest implementation challenges in radio communication transmitters. Among various linearization methods, the feedforward linearization technique is a fairly established principle offering a good tradeoff between linearity and power-efficiency even under wideband operation. Moreover, adaptive techniques for such linearizer have been proposed in literature to track parameter changes in the main PA and other circuitry. Among those, least mean squares (LMS) method for adapting signal cancellation loop (SCL) and error cancellation loop (ECL) coefficients is an attractive low-complexity alternative. In this paper, we carry out extensive closed-form performance analysis of the achievable intermodulation distortion (IMD) reduction of the overall LMS-adaptive feedforward linearizer, as a function of the used step-sizes and essential waveform statistics. Such analysis is currently missing from the state-of-the-art literature. Both memoryless nonlinearities and Wiener-Hammerstein type PA memory models are studied for which IMD suppression expressions are derived. Comprehensive computer simulations are also provided to illustrate the accuracy of the analysis when practical OFDM waveforms are used. Design examples are given as well where the analysis results are used to choose proper linearizer step-sizes to meet given transmitter spectral mask specifications.
引用
收藏
页码:82 / 99
页数:18
相关论文
共 50 条
  • [1] Convergence and steady-state analysis of a variable step-size Normalized LMS algorithm
    Sulyman, AI
    Zerguine, A
    SEVENTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOL 2, PROCEEDINGS, 2003, : 591 - 594
  • [2] Transient and Steady-State Analysis of Adaptive Step-Size Least Mean Modulus Algorithm
    Koike, Shin'ichi
    2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATIONS SYSTEMS (ISPACS 2008), 2008, : 22 - 25
  • [3] New Steady-State Analysis Results of Variable Step-Size LMS Algorithm With Different Noise Distributions
    Zhang, Sheng
    Zhang, Jiashu
    IEEE SIGNAL PROCESSING LETTERS, 2014, 21 (06) : 653 - 657
  • [4] Convergence and steady-state analysis of a variable step-size NLMS algorithm
    Sulyman, AI
    Zerguine, A
    SIGNAL PROCESSING, 2003, 83 (06) : 1255 - 1273
  • [5] Improved Variable Step-size LMS for Digital Predistortion in Wideband Power Amplifiers
    You, Qidi
    Gu, Linhai
    Liu, Jiangchun
    2018 8TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC), 2018, : 116 - 119
  • [6] Steady-state analysis of the long LMS adaptive filter
    Butterweck, H. J.
    SIGNAL PROCESSING, 2011, 91 (04) : 690 - 701
  • [7] Adaptive Fourier Analysis Using a Variable Step-Size LMS Algorithm
    Xiao, Yegui
    Huang, Boyan
    Wei, Hongyun
    2013 9TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING (ICICS), 2013,
  • [8] Steady state performance analysis of an approximately optimal variable step size LMS algorithm
    Li Ning
    Zhang Yonggang
    Hao Yanling
    ICIEA 2008: 3RD IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, PROCEEDINGS, VOLS 1-3, 2008, : 1379 - 1382
  • [9] Performance of Variable Step-size LMS algorithms for linear adaptive inverse control systems
    Yang, TB
    SCONEST 2004: STUDENT CONFERENCE ON ENGINEERING SCIENCES AND TECHNOLOGY, 2002, : 122 - 126
  • [10] An Adaptive Regularized Subspace Pursuit based Variable Step-size Method for Power Amplifier Sparse Model Selection
    Wang, Fen
    Yu, Cuiping
    Li, Shulan
    Su, Ming
    Liu, Yuanan
    2021 IEEE MTT-S INTERNATIONAL WIRELESS SYMPOSIUM (IWS 2021), 2021,