高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进的稀疏最小二乘双子支撑向量回归的数字预失真技术

代志江 孔淑曼 李明玉 蔡天赋 靳一 徐常志

代志江, 孔淑曼, 李明玉, 蔡天赋, 靳一, 徐常志. 基于改进的稀疏最小二乘双子支撑向量回归的数字预失真技术[J]. 电子与信息学报, 2023, 45(2): 418-426. doi: 10.11999/JEIT220372
引用本文: 代志江, 孔淑曼, 李明玉, 蔡天赋, 靳一, 徐常志. 基于改进的稀疏最小二乘双子支撑向量回归的数字预失真技术[J]. 电子与信息学报, 2023, 45(2): 418-426. doi: 10.11999/JEIT220372
DAI Zhijiang, KONG Shuman, LI Mingyu, CAI Tianfu, JIN Yi, XU Changzhi. A Digital Predistortion Technique Based on Improved Sparse Least Squares Twin Support Vector Regression[J]. Journal of Electronics & Information Technology, 2023, 45(2): 418-426. doi: 10.11999/JEIT220372
Citation: DAI Zhijiang, KONG Shuman, LI Mingyu, CAI Tianfu, JIN Yi, XU Changzhi. A Digital Predistortion Technique Based on Improved Sparse Least Squares Twin Support Vector Regression[J]. Journal of Electronics & Information Technology, 2023, 45(2): 418-426. doi: 10.11999/JEIT220372

基于改进的稀疏最小二乘双子支撑向量回归的数字预失真技术

doi: 10.11999/JEIT220372
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:

    代志江:男,讲师,博士,研究方向为无线通信射频发射机设计

    孔淑曼:女,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

    蔡天赋:男,博士生,研究方向为超宽带数字预失真

    靳一:男,博士,研究方向为卫星通信与网络

    徐常志:男,研究员,博士,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 Improved Sparse Least Squares Twin Support Vector Regression

Funds: The National Natural Science Foundation of China (62171068, 62001061), The QingNian Project of Science and Technology Research Program of Chongqing Education Commission of China (KJQN201902403)
  • 摘要: 为了补偿大容量卫星通信射频前端的功率放大器的非线性,传统的数字预失真(DPD)模型需要更多的系数和更高的阶次,严重影响预失真前馈路径的资源消耗。为了解决这一问题,该文提出一种基于改进的稀疏最小二乘双子支撑向量回归(ISLSTSVR)的低复杂度DPD方法。首先通过构建原空间的决策函数解决最小二乘双子支撑向量回归(LSTSVR)模型解不稀疏的问题;同时引用截断最小二乘损失函数增加模型的鲁棒性;然后采用Nystrom逼近方法得到核矩阵的低秩近似,进一步采用Cholesky分解降低核矩阵的运算复杂度;最后由低秩的核矩阵求得模型稀疏解。实验选用基于单管氮化镓(GaN)器件的宽带AB类功率放大器,以40 MHz的32QAM信号进行激励。预失真实验表明,该方法能在保证模型精度的情况下大幅减少DPD模型系数和计算复杂度,为星载射频前端的预失真技术提供了有效的系数降维思路和方法。
  • 图  1  ISLSTSVR算法流程图

    图  2  预失真系统框图

    图  3  实验平台现场图

    图  4  ISLSTSVR逆模预失真的AM/AM, AM/PM曲线

    图  5  不同模型预失真输出信号的功率谱密度对比

    表  1  不同样本集大小的机器学习模型建模效果对比

    模型样本数训练时间(s)NMSE(dB)模型系数
    GMP80000.63–44.13148
    120000.73–45.15148
    SVR80001319.00–46.297386
    120002599.70–48.0211081
    TSVR8000531.43–46.917332
    120002024.80–47.6110753
    160005017.10–47.8914316
    STSVR8000156.02–47.272002
    12000456.54–48.123002
    16000940.72–48.984002
    ISLSTSVR800019.97–47.23788
    1200049.37–48.19874
    1600096.95–48.92940
    下载: 导出CSV

    表  2  不同模型预失真性能比较

    预失真器ACPR(dB)
    –30 MHz+30 MHz
    原始功放输出–39.68–41.23
    TSVR预失真输出–57.19–57.20
    STSVR预失真输出–58.89–58.95
    ISLSTSVR预失真输出–60.28–61.00
    下载: 导出CSV
  • [1] BEIDAS B F. Radio-frequency impairments compensation in ultra high-throughput satellite systems[J]. IEEE Transactions on Communications, 2019, 67(9): 6025–6038. doi: 10.1109/TCOMM.2019.2926031
    [2] DOSHI R J, GHODGAONKAR D, BHARDHWAJ P S, et al. Accurate characterization of high power SSPA under multicarrier operation for satellite communications[C]. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kerala, India, 2017: 818–823.
    [3] 黄明光, 朱丹, 何俊, 等. Ka波段宽频带高线性空间行波管研制[J]. 电子与信息学报, 2017, 39(11): 2777–2781. doi: 10.11999/JEIT161267

    HUANG Mingguang, ZHU Dan, HE Jun, et al. Development of Ka-band broadband, high-linearity space traveling wave tube[J]. Journal of Electronics &Information Technology, 2017, 39(11): 2777–2781. doi: 10.11999/JEIT161267
    [4] CHEN Wenhua, ZHANG Silong, LIU Youjiang, et al. Efficient pruning technique of memory polynomial models suitable for PA behavioral modeling and digital predistortion[J]. IEEE Transactions on Microwave Theory and Techniques, 2014, 62(10): 2290–2299. doi: 10.1109/TMTT.2014.2351779
    [5] ABDELHAFIZ A, KWAN A, HAMMI O, et al. Digital predistortion of LTE-A power amplifiers using compressed-sampling-based unstructured pruning of Volterra series[J]. IEEE Transactions on Microwave Theory and Techniques, 2014, 62(11): 2583–2593. doi: 10.1109/TMTT.2014.2360845
    [6] REINA-TOSINA J, ALLEGUE-MARTÍNEZ M, CRESPO-CADENAS C, et al. Behavioral modeling and predistortion of power amplifiers under sparsity hypothesis[J]. IEEE Transactions on Microwave Theory and Techniques, 2015, 63(2): 745–753. doi: 10.1109/TMTT.2014.2387852
    [7] LI Mingyu, YANG Zhenxing, ZHANG Zhongming, et al. Sparsity adaptive estimation of memory polynomial based models for power amplifier behavioral modeling[J]. IEEE Microwave and Wireless Components Letters, 2016, 26(5): 370–372. doi: 10.1109/LMWC.2016.2549024
    [8] BECERRA J A, MADERO-AYORA M J, REINA-TOSINA J, et al. A doubly orthogonal matching pursuit algorithm for sparse predistortion of power amplifiers[J]. IEEE Microwave and Wireless Components Letters, 2018, 28(8): 726–728. doi: 10.1109/LMWC.2018.2845947
    [9] 赵辉, 莫谨荣, 王薇, 等. OFDM系统中基于压缩感知的非线性失真恢复研究[J]. 电子与信息学报, 2021, 43(7): 1907–1912. doi: 10.11999/JEIT200374

    ZHAO Hui, MO Jinrong, WANG Wei, et al. Research on nonlinear distortion recovery based on compressed sensing in OFDM system[J]. Journal of Electronics &Information Technology, 2021, 43(7): 1907–1912. doi: 10.11999/JEIT200374
    [10] BECERRA J A, MADERO-AYORA M J, REINA-TOSINA J, et al. A reduced-complexity doubly orthogonal matching pursuit algorithm for power amplifier sparse behavioral modeling[C]. 2019 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR), Orlando, USA, 2019: 1–3.
    [11] BECERRA J A, AYORA M J M, REINA-TOSINA J, et al. Sparse identification of Volterra models for power amplifiers without pseudoinverse computation[J]. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(11): 4570–4578. doi: 10.1109/TMTT.2020.3016967
    [12] CRESPO-CADENAS C, MADERO-AYORA M J, BECERRA J A, et al. A fast sparse Bayesian pursuit approach for power amplifier linearization[C]. 2021 IEEE MTT-S International Wireless Symposium (IWS), Nanjing, China, 2021: 1–3.
    [13] PENG Jun, HE Songbai, WANG Bingwen, et al. Digital predistortion for power amplifier based on sparse Bayesian learning[J]. IEEE Transactions on Circuits and Systems II:Express Briefs, 2016, 63(9): 828–832. doi: 10.1109/TCSII.2016.2534718
    [14] BECERRA J A, MADERO-AYORA M J, NOGUER R G, et al. On the optimum number of coefficients of sparse digital predistorters: A Bayesian approach[J]. IEEE Microwave and Wireless Components Letters, 2020, 30(12): 1117–1120. doi: 10.1109/LMWC.2020.3027878
    [15] LIU Zhijun, HU Xin, WANG Weidong, et al. A joint PAPR reduction and digital predistortion based on real-valued neural networks for OFDM systems[J]. IEEE Transactions on Broadcasting, 2022, 68(1): 223–231. doi: 10.1109/TBC.2021.3132158
    [16] WANG Zonghao, CHEN Wenhua, SU Gongzhe, et al. Low computational complexity digital predistortion based on direct learning with covariance matrix[J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 65(11): 4274–4284. doi: 10.1109/TMTT.2017.2690290
    [17] ZHANG Yikang, LI Gang, LI Hongmin, et al. Simplified vector decomposition time-delay neural network model for RF power amplifier modeling and digital predistortion[C]. 2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT), Nanjing, China, 2021: 1–3.
    [18] CAI Jialin, YU Chao, SUN Lingling, et al. Dynamic behavioral modeling of RF power amplifier based on time-delay support vector regression[J]. IEEE Transactions on Microwave Theory and Techniques, 2019, 67(2): 533–543. doi: 10.1109/TMTT.2018.2884414
    [19] XU Jin, JIANG Weiliang, MA Linhua, et al. Augmented time-delay twin support vector regression-based behavioral modeling for digital predistortion of RF power amplifier[J]. IEEE Access, 2019, 7: 59832–59843. doi: 10.1109/ACCESS.2019.2915281
    [20] CAI Tianfu, LI Mingyu, YAO Yao, et al. An improved nonlinear smooth twin support vector regression based-behavioral model for joint compensation of frequency-dependent transmitter nonlinearities[J]. International Journal of RF and Microwave Computer-Aided Engineering, 2021, 31(6): e22636. doi: 10.1002/mmce.22636
    [21] CHAPELLE O. Training a support vector machine in the primal[J]. Neural Computation, 2007, 19(5): 1155–1178. doi: 10.1162/neco.2007.19.5.1155
    [22] SUYKENS J A K and VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293–300. doi: 10.1023/A:1018628609742
    [23] CHEN Li and ZHOU Shuisheng. Sparse algorithm for robust LSSVM in primal space[J]. Neurocomputing, 2018, 275: 2880–2891. doi: 10.1016/j.neucom.2017.10.011
    [24] ZHOU Shuisheng. Sparse LSSVM in primal using Cholesky factorization for large-scale problems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(4): 783–795. doi: 10.1109/TNNLS.2015.2424684
  • 加载中
图(5) / 表(2)
计量
  • 文章访问数:  389
  • HTML全文浏览量:  175
  • PDF下载量:  120
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-31
  • 修回日期:  2022-08-06
  • 录用日期:  2022-08-09
  • 网络出版日期:  2022-08-10
  • 刊出日期:  2023-02-07

目录

    /

    返回文章
    返回