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Block-Oriented Time-Delay Neural Network Behavioral Model for Digital Predistortion of RF Power Amplifiers

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  • DOI number:10.1109/TMTT.2021.3124211

  • Affiliation of Author(s):中国科学技术大学

  • Teaching and Research Group:电子工程与信息科学系

  • Journal:IEEE Transactions on Microwave Theory and Techniques

  • Funded by:国家自然科学基金 61471333

  • Key Words:Block-oriented models, digital predistortion (DPD), low feedback sampling rate, neural network, power amplifiers (PAs).

  • Abstract:A novel block-oriented time-delay neural network (BOTDNN) model for dynamic nonlinear modeling and digital
    predistortion (DPD) of RF power amplifiers (PAs) is proposed.
    The proposed model consists of a dynamic linear network and
    a static nonlinear network to characterize dynamic nonlinear
    systems. The dynamic linear network simulates multiple linear
    filters using a fully connected layer with the linear activation
    function. The static nonlinear network is constructed based on
    vector decomposition and phase recovery mechanism. To validate
    the proposed model, experiments have been carried out with
    two different PAs operating at 2.4 and 39 GHz, respectively.
    The test results demonstrate that the proposed model has better
    PA modeling and nonlinear compensation capabilities than state-of-the-art PA behavioral models, while with significantly lower model complexity. Furthermore, to reduce the system cost,
    we investigate the problems that arise when the neural network-based behavioral models are applied to low feedback sampling
    rate DPD and propose an improved method. The experiments
    confirm that the proposed low feedback sampling rate DPD
    method can effectively alleviate the deterioration of linearization
    performance caused by undersampling.

  • First Author:Chengye Jiang (姜成业)

  • Co-author:Hongmin Li,Wen Qiao,Guichen Yang,Qiao Liu,Guangjian Wang

  • Indexed by:Journal paper

  • Correspondence Author:Falin Liu

  • Document Code:10.1109/TMTT.2021.3124211

  • Discipline:Engineering

  • Document Type:J

  • Volume:70

  • Issue:3

  • Page Number:1461-1473

  • Date of Publication:2022-03-01

  • Included Journals:SCI、EI

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