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一种基于维度加权盲K近邻算法的数字预失真技术

蒋卫恒 段耀星 李明玉 靳一 徐常志 李立

蒋卫恒, 段耀星, 李明玉, 靳一, 徐常志, 李立. 一种基于维度加权盲K近邻算法的数字预失真技术[J]. 电子与信息学报. doi: 10.11999/JEIT220302
引用本文: 蒋卫恒, 段耀星, 李明玉, 靳一, 徐常志, 李立. 一种基于维度加权盲K近邻算法的数字预失真技术[J]. 电子与信息学报. doi: 10.11999/JEIT220302
JIANG Weiheng, DUAN Yaoxing, LI Mingyu, JIN Yi, XU Changzhi, LI Li. A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT220302
Citation: JIANG Weiheng, DUAN Yaoxing, LI Mingyu, JIN Yi, XU Changzhi, LI Li. A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT220302

一种基于维度加权盲K近邻算法的数字预失真技术

doi: 10.11999/JEIT220302
基金项目: 国家自然科学基金(62171068, 62001061),重庆市教委科技研究专项青年项目(KJQN201902403)
详细信息
    作者简介:

    蒋卫恒:男,副研究员,博士,研究方向为无线通信与网络

    段耀星:男,硕士生,研究方向为超宽带数字预失真

    李明玉:男,教授,博士生导师,研究方向为射频电路与系统

    靳一:男,研究员,研究方向为卫星通信与网络

    徐常志:男,研究员,研究方向为卫星通信与网络

    李立:男,研究员,研究方向为卫星通信与网络

    通讯作者:

    李明玉 myli@cqu.edu.cn

  • 中图分类号: TN911.3

A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm

Funds: The National Natural Science Foundation of China (62171068, 62001061), QingNian Project of Science and Technology Research Program of Chongqing Education Commission of China(KJQN201902403)
  • 摘要: 传统的数字预失真(DPD)模型通常在所有的输入信号功率上采用单一多项式模型和单一记忆深度对功率放大器(PA)进行线性化矫正。然而,功率放大器在不同的功率水平下会呈现出不同的非线性特性,并产生不同的记忆效应。针对这一问题,该文提出一种基于维度加权盲K近邻(KNN)算法的数字预失真模型,所提模型根据功放当前输入信号以及记忆输入信号的幅度进行维度加权的KNN分类,并为每一类输入信号序列建立子模型。所提方法用Doherty功率放大器进行实验验证,使用带宽为30 MHz、频点为2.2 GHz的3载波长期演进(LTE)信号作为输入,反馈端使用122.88 MHz的采样率进行采样。实验结果表明,所提维度加权盲KNN分类方法与记忆多项式(MP)模型结合时,功放正向建模效果和数字预失真效果均超过了广义记忆多项式(GMP)模型,并远超记忆多项式模型的效果,实验结果验证了所提模型的优良性能。
  • 图  1  维度加权的盲KNN算法流程

    (b)分类模型

    图  2  WKMP模型DPD结构框图

    图  3  实验平台现场图

    图  4  WKMP建模功率谱

    图  5  建模精度和子模型数量的关系

    图  6  4种模型DPD输出信号功率谱

    图  7  WKMP模型AM-AM和PM-PM曲线

    表  1  3种模型PA正向建模精度对比(dB)

    MPGMPKMPWKMP
    建模精度NMSE–36.72–47.99–45.31–50.67
    下载: 导出CSV

    表  2  各模型的DPD性能对比

    无预失真记忆多项式广义记忆多项式K近邻记忆多项式维度加权K近邻记忆多项式
    线性化效果ACPR(dBc)–34.72/–33.00–44.68/–45.16–47.53/–46.96–47.37/–46.36–48.18/–48.34
    还原效果NMSE(dB)–11.20–31.47–30.65–34.76–43.28
    下载: 导出CSV

    表  3  各模型的计算复杂度对比

    预失真器$ 1/D $${\boldsymbol{H\omega}}$$({{\boldsymbol{H}}}^{ {\rm H} }{\boldsymbol{H}}{)}^{-1}{{\boldsymbol{H}}}^{{\rm{H}}}{\boldsymbol{e}}$总计算量
    MP0${ {{NP} }_{\rm{MP} } }$$ NP_{\rm{MP}}^2 + P_{\rm{MP}}^3 + P_{\rm{MP}}^2 + N{P_{\rm{MP}}} $4,460,800
    GMP0${ {{NP} }_{ {\rm{GMP} } } }$$NP_{{\rm{GMP}}}^2 + P_{{\rm{GMP}}}^3 + {P}_{ {{\rm{GMP}}} }^{2}{ + N}{ {P}_{ {{\rm{GMP}}} } }$140,968,656
    KMP$ K(4N + N) $${ { {{NP} } }_{\rm{ {KMP} } } }$$ \displaystyle\sum {{(NP}{{_{\rm{{KMP}}}^{2}}_i}{ + P}_{\rm{{KMP}}}^{3}{ + P}_{\rm{{KMP}}}^{2}{ + }{{N}_i}{{P}_{\rm{{KMP}}}}} {)} $45,451,264
    WKMP$ K(4N + N) $${ {{NP} }_{\rm{WKMP} } }$$ \displaystyle\sum {{(}{{N}_j}{P}_{{\rm{WKMP}}}^{2}{ + P}_{{\rm{WKMP}}}^{3}{ + P}_{{\rm{WKMP}}}^{2}{ + }{{N}_j}{{P}_{{\rm{WKMP}}}}} {)} $45,451,264
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-21
  • 修回日期:  2022-09-19
  • 网络出版日期:  2022-09-27

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