• 北大核心期刊(《中文核心期刊要目总览》2017版)
  • 中国科技核心期刊(中国科技论文统计源期刊)
  • JST 日本科学技术振兴机构数据库(日)收录期刊

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

基于变换域的迭代MPD算法

周睿 李英善

周睿, 李英善. 基于变换域的迭代MPD算法[J]. 微电子学与计算机, 2022, 39(8): 71-77. doi: 10.19304/J.ISSN1000-7180.2021.1042
引用本文: 周睿, 李英善. 基于变换域的迭代MPD算法[J]. 微电子学与计算机, 2022, 39(8): 71-77. doi: 10.19304/J.ISSN1000-7180.2021.1042
ZHOU Rui, LI Yingshan. Iterative MPD algorithm based on transform domain[J]. Microelectronics & Computer, 2022, 39(8): 71-77. doi: 10.19304/J.ISSN1000-7180.2021.1042
Citation: ZHOU Rui, LI Yingshan. Iterative MPD algorithm based on transform domain[J]. Microelectronics & Computer, 2022, 39(8): 71-77. doi: 10.19304/J.ISSN1000-7180.2021.1042

基于变换域的迭代MPD算法

doi: 10.19304/J.ISSN1000-7180.2021.1042
基金项目: 

国家自然科学基金联合基金项目 U2031208

详细信息
    作者简介:

    周睿  女,(1994-),硕士研究生.研究方向为无线通信

    通讯作者:

    李英善(通讯作者)   女,(1972-),博士,副教授.研究方向为无线通信.E-mail: yingsl1122@nankai.edu.cn

  • 中图分类号: TN929.5

Iterative MPD algorithm based on transform domain

  • 摘要:

    相较于传统正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)技术,滤波OFDM(Filtered OFDM, F-OFDM)技术具有子载波带宽灵活可变和抑制频谱带外泄露等优点,是面向未来无线通信系统的候选波形之一.但是在高动态通信场景下,时变信道中存在的多普勒频偏现象却依然会严重损害F-OFDM系统的性能.针对该问题,提出一种基于变换域的迭代消息传递检测(Message Passing Detection, MPD)算法.MPD算法基于稀疏因子图,通过在收发节点间进行迭代式的消息传递和状态更新,最终实现对多普勒频偏的抑制.此外,通过对检测过程中的干扰进行高斯等效,能够一定程度上降低迭代MPD检测算法的复杂度.进一步的,所提算法基于变换域的设计思路,能够充分利用时变多径信道在变换域的增强型稀疏性,以此有效减少MPD算法中收发节点间的连接支路数,进而降低检测算法的计算复杂度.基于F-OFDM系统的仿真结果表明,相较于传统的时频域MPD算法,所提基于变换域的迭代MPD算法在系统误码率和计算复杂度上均有更为优异的性能表现.

     

  • 图 1  F-OFDM系统模型

    Figure 1.  F-OFDM system model

    图 2  基于时频域的和时延-多普勒变换域的时变多径信道表征

    Figure 2.  Time-varying multipath channel based on time-frequency domain and delay-Doppler domain

    图 3  基于不同域表征的等效信道矩阵非零元素占比

    Figure 3.  The ratio of the non-zero elements of the effective channel matrix based on different domains

    图 4  BER性能对比

    Figure 4.  The comparison of BER performance

  • [1] GUAN P, WU D, TIAN T J, et al. 5G field trials: OFDM-based waveforms and mixed numerologies[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(6): 1234-1243. DOI: 10.1109/JSAC.2017.2687718.
    [2] BALINT C, BUDURA G. OFDM-based multi-carrier waveforms performances in 5G[C]//International Symposium on Electronics and Telecommunications (ISETC). Timisoara: IEEE, 2018: 1-4. DOI: 10.1109/ISETC.2018.8583966.
    [3] FETTWEIS G, KRONDORF M, BITTNER S. GFDM-generalized frequency division multiplexing[C]//IEEE 69th Vehicular Technology Conference. Barcelona: IEEE, 2009: 1-4. DOI: 10.1109/VETECS.2009.5073571.
    [4] SCHAICH F, WILD T. Waveform contenders for 5G-OFDM vs. FBMC vs. UFMC[C]//6th International Symposium on Communications, Control and Signal Processing (ISCCSP). Athens: IEEE, 2014: 457-460. DOI: 10.1109/ISCCSP.2014.6877912.
    [5] 吴蓉, 罗志年. 基于加权的FBMC系统信道估计新算法[J]. 微电子学与计算机, 2020, 37(1): 66-71. DOI: 10.19304/j.cnki.issn1000-7180.2020.01.011.

    WU R, LUO Z N. New channel estimation method for FBMC system based on weighed[J]. Microelectronics & Computer, 2020, 37(1): 66-71. DOI: 10.19304/j.cnki.issn1000-7180.2020.01.011.
    [6] KHAN B, VELEZ F J. Multicarrier Waveform candidates for beyond 5G[C]//12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). Porto: IEEE, 2020: 1-6. DOI: 10.1109/CSNDSP49049.2020.9249568.
    [7] 陈曦, 吴天宝, 龚奕宇, 等. 一种低复杂度近最优大规模MIMO检测算法[J]. 微电子学与计算机, 2020, 37(10): 48-53. DOI: 10.19304/j.cnki.issn1000-7180.2020.10.009.

    CHEN X, WU T B, GONG Y Y, et al. A low-complexity and near-optimal massive MIMO detection algorithm[J]. Microelectronics & Computer, 2020, 37(10): 48-53. DOI: 10.19304/j.cnki.issn1000-7180.2020.10.009.
    [8] SOM P, DATTA T, SRINIDHI N, et al. Low-complexity detection in large-dimension MIMO-ISI channels using graphical models[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(8): 1497-1511. DOI: 10.1109/JSTSP.2011.2166950.
    [9] SCHNITER P. A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(8): 1462-1474. DOI: 10.1109/JSTSP.2011.2169232.
    [10] ZHU H C, LIN J, WANG Z F. Reduced complexity message passing detection algorithm in large-scale MIMO systems[C]//9th International Conference on Wireless Communications and Signal Processing (WCSP). Nanjing: IEEE, 2017: 1-5. DOI: 10.1109/WCSP.2017.8170973.
    [11] ZOU Q F, TAN X Z, LIU M, et al. Main-branch structure iterative detection using approximate message passing for uplink large-scale multiuser MIMO systems[J]. International Journal of Antennas and Propagation, 2016, 2016: 2832584. DOI: 10.1155/2016/2832584.
    [12] 任茜源, 郑兴林. 大规模MIMO系统中低复杂度信号检测算法[J]. 光通信研究, 2020, (2): 67-72. DOI: 10.13756/j.gtxyj.2020.02.014.

    REN X Y, ZHENG X L. Low complexity signal detection algorithms for massive MIMO systems[J]. Study on Optical Communications, 2020, (2): 67-72. DOI: 10.13756/j.gtxyj.2020.02.014.
    [13] GUO W, ZHANG W L, MU P C, et al. High-mobility wideband massive MIMO communications: Doppler compensation, analysis and scaling laws[J]. IEEE Transactions on Wireless Communications, 2019, 18(6): 3177-3191. DOI: 10.1109/TWC.2019.2911508.
    [14] SHEN W Q, DAI L L, AN J P, et al. Channel estimation for orthogonal time frequency space (OTFS) massive MIMO[J]. IEEE Transactions on Signal Processing, 2019, 67(16): 4204-4217. DOI: 10.1109/TSP.2019.2919411.
    [15] BELLO P. Characterization of randomly time-variant linear channels[J]. IEEE transactions on Communications Systems, 1963, 11(4): 360-393. DOI: 10.1109/TCOM.1963.1088793.
    [16] JANSSEN A J E M. The Zak transform: a signal transform for sampled time-continuous signals[J]. Philips Journal of Research, 1988, 43(1): 23-69.
    [17] JAKES W C. Microwave mobile communications[M]. New York: Wiley, 1974.
  • 加载中
图(4)
计量
  • 文章访问数:  82
  • HTML全文浏览量:  40
  • PDF下载量:  7
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-19
  • 修回日期:  2021-11-01
  • 网络出版日期:  2022-08-15

目录

    /

    返回文章
    返回