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基于最优索引广义正交匹配追踪的非正交多址系统多用户检测

申滨 吴和彪 崔太平 陈前斌

引用本文: 申滨, 吴和彪, 崔太平, 陈前斌. 基于最优索引广义正交匹配追踪的非正交多址系统多用户检测[J]. 电子与信息学报, 2020, 42(3): 621-628. doi: 10.11999/JEIT190270 shu
Citation:  Bin SHEN, Hebiao WU, Taiping CUI, Qianbin CHEN. An Optimal Number of Indices Aided gOMP Algorithm for Multi-user Detection in NOMA System[J]. Journal of Electronics and Information Technology, 2020, 42(3): 621-628. doi: 10.11999/JEIT190270 shu

基于最优索引广义正交匹配追踪的非正交多址系统多用户检测

    作者简介: 申滨: 男,1978年生,教授,研究方向为认知无线电、大规模MIMO等;
    吴和彪: 男,1994年生,硕士生,研究方向为免调度NOMA多用户检测;
    崔太平: 男,1981年生,讲师,研究方向为认知无线电、车联网等;
    陈前斌: 男,1967年生,教授、博士生导师,研究方向为下一代网络、个人通信等
    通讯作者: 申滨,shenbin@cqupt.edu.cn
  • 基金项目: 国家自然科学基金(61571073)

摘要: 作为5G的关键技术之一,非正交多址(NOMA)通过非正交方式访问无线通信资源,以实现提高频谱利用率、增加用户连接数的目的。该文提出将压缩感知(CS)及广义正交匹配追踪(gOMP)算法引入上行免调度NOMA系统,从而增强NOMA系统活跃用户检测及数据接收的性能。通过每次迭代识别多个索引,gOMP算法实际上是传统的正交匹配追踪(OMP)算法的扩展。为了获得最优性能,研究分析了在gOMP算法信号重构的每次迭代中所应选择的最优索引数目。仿真结果表明:与其它的贪婪追踪算法及梯度投影稀疏重构(GPSR)算法相比,最优索引gOMP算法具有更优异的信号重构性能;并且,对于不同的活跃用户数或过载率等参数配置的NOMA系统,均表现出最优的多用户检测性能。

English

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      缪祥华, 单小撤. 基于密集连接卷积神经网络的入侵检测技术研究. 电子与信息学报, 2020, 41(0): 1-7.

  • 图 1  稀疏度和索引数目对常数$\delta $的影响

    图 2  稀疏度对gOMP精确重构概率的影响

    图 3  稀疏度对不同算法精确重构概率的影响

    图 4  取不同索引数目时,gOMP算法的BER性能

    图 5  6种贪婪算法的BER性能

    图 6  5种多用户检测算法BER性能对比

    图 7  活跃用户数对BER性能的影响

    图 8  过载率对BER性能的影响

    表 1  最优索引gOMP检测算法

     算法1 最优索引gOMP检测算法
     输入 ${{y}}$, ${{H}}$, $S$, ${C_{{\rm{opt}}}}$.
     初始化:${{{r}}^0} = {{y}}$,${{{\varGamma}} ^0} = \varnothing $,$t = 0$.
     (1) While ${\left\| {{{{r}}^t}} \right\|_2} > e$ 且$t \le S$ do
     (2) $t = t + 1$;
     (3) $\eta {\rm{(}}i{\rm{)}} = \mathop {{\rm{argmax}}}\limits_{j:j \in \varOmega \backslash {\rm{\{ }}\eta {\rm{(}}i - 1{\rm{)}}, \cdots ,\eta {\rm{(2)}},\eta {\rm{(}}1{\rm{)\} }}} \left| { < {{{r}}^{t - 1}},{{{\varphi}} _j} > } \right|$;
     (4) ${{{\varGamma}} ^t} = {{{\varGamma}} ^{t - 1}} \cup {\rm{\{ }}\eta {\rm{(1),}}\eta {\rm{(2),}} ··· ,\eta {\rm{(}}{C_{{\rm{opt}}}}{\rm{)\} }}$;
     (5) ${\hat {{x} }_{ { {{\varGamma} } ^t} } } = \mathop { {\rm{argmin} } }\limits_{ u} {\left\| { {{y} } - { {{H} }_{ { {{\varGamma} } ^t} } }{{u} } } \right\|_2} = {{H} }_{ { {{\varGamma} } ^t} }^{\rm{† } }{{y} }$;
     (6) ${{{r}}^t} = {{y}} - {{{H}}_{{{{\varGamma}} ^t}}}{\hat {{x}}_{{{{\varGamma}} ^t}}}$
       end while
     输出 ${\hat {{x} }_{ { {{\varGamma} } ^t} } } = \mathop { {\rm{argmin} } }\limits_{ u} {\left\| { {{y} } - { {{H} }_{ { {{\varGamma} } ^t} } }{{u} } } \right\|_2}$
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文章相关
  • 通讯作者:  申滨, shenbin@cqupt.edu.cn
  • 收稿日期:  2019-04-18
  • 录用日期:  2019-07-28
  • 网络出版日期:  2019-07-31
  • 刊出日期:  2020-03-01
通讯作者: 陈斌, bchen63@163.com
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