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可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法

刘期烈 辛雅楠 高俊鹏 周继华 黄东 赵涛

刘期烈, 辛雅楠, 高俊鹏, 周继华, 黄东, 赵涛. 可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法[J]. 电子与信息学报, 2022, 44(7): 2332-2341. doi: 10.11999/JEIT210521
引用本文: 刘期烈, 辛雅楠, 高俊鹏, 周继华, 黄东, 赵涛. 可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法[J]. 电子与信息学报, 2022, 44(7): 2332-2341. doi: 10.11999/JEIT210521
LIU Qilie, XIN Yanan, GAO Junpeng, ZHOU Jihua, HUANG Dong, ZHAO Tao. Robust Energy Efficiency Resource Allocation Algorithm in Reconfigurable Intelligent Surface-assisted Non-Orthogonal Multiple Access Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2332-2341. doi: 10.11999/JEIT210521
Citation: LIU Qilie, XIN Yanan, GAO Junpeng, ZHOU Jihua, HUANG Dong, ZHAO Tao. Robust Energy Efficiency Resource Allocation Algorithm in Reconfigurable Intelligent Surface-assisted Non-Orthogonal Multiple Access Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2332-2341. doi: 10.11999/JEIT210521

可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法

doi: 10.11999/JEIT210521
基金项目: 重庆市自然基金(cstc2019jcyj-zdxm0008),重庆市科技创新领军人才支持计划(CSTCCXLJRC201908),重庆市教委重点项目(KJZD-K201900605)
详细信息
    作者简介:

    刘期烈:男,1974年生,博士,博士生导师,教授,研究方向为无线传感器网络、无线Mesh网络、卫星通信、车载网络、UWB室内定位、大数据等

    辛雅楠:女,1996年生,硕士生,研究方向为可重构智能表面技术、无线网络资源分配

    高俊鹏:男,1988年生,博士生,研究方向为可重构智能表面技术

    周继华:男,1979年生,博士,博士生导师,研究员,研究方向为通信网络、无线通信、数据链、智能集群

    黄东:男,1981年生,博士,博士生导师,教授,研究方向为无线通信、5G、6G等

    赵涛:男,1983年生,硕士,研究员,研究方向为无线自组网、5G、6G等

    通讯作者:

    周继华 jhzhou@ict.ac.cn

  • 中图分类号: TN92

Robust Energy Efficiency Resource Allocation Algorithm in Reconfigurable Intelligent Surface-assisted Non-Orthogonal Multiple Access Networks

Funds: The Basic and Advanced Research Projects of CSTC (cstc2019jcyj-zdxm0008), The Chongqing Science and Technology Innovation Leading Talent Support Program (CSTCCXLJRC201908), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K201900605)
  • 摘要: 为提高非正交多址接入(NOMA)网络的鲁棒性和系统能效(EE),考虑了不完美信道状态信息,该文提出一种可重构智能表面(RIS)辅助的NOMA网络鲁棒能效最大资源分配算法。考虑用户信干噪比(SINR)中断概率约束、基站的最大发射功率约束以及连续相移约束,建立了一个非线性的能效最大化资源分配模型。用Dinkelbach方法将分式形式的目标函数转换为线性的参数相减的形式,利用S-procedure方法将含有信道不确定性的SINR中断概率约束转换成确定性形式,利用交替优化算法将多变量耦合的非凸优化问题分解成多个凸优化子问题,最后用CVX对分解出的子问题进行求解。仿真结果表明,在EE方面,所提算法比无可重构智能表面(RIS)算法提高了7.4%。在SINR中断概率方面,所提算法比非鲁棒算法降低了85.5%。
  • 图  1  RIS辅助的NOMA网络

    图  2  3D示意图

    图  3  算法1的收敛性能

    图  4  系统能效与最大发射功率的关系

    图  5  系统能效与发射天线数的关系

    图  6  系统能效与中断概率的关系

    图  7  系统能效与用户目标SINR的关系

    图  8  中断概率与用户目标SINR的关系

    表  1  系统参数描述

    参数含义参数含义
    $ K $用户数$ \xi $发射功率放大器的效率
    $ M $基站天线数$ {s}_{k} $基站发给用户k的期望信号
    $N$RIS反射振元数${{\boldsymbol{w}}_k}$基站到用户k的波束成形向量
    ${\boldsymbol{G}}$BS到RIS的信道$ {\rho _k} $用户k的SINR小于目标SINR的概率
    ${{\boldsymbol{h}}_k}$BS到用户k的信道${P_{{\text{BS}}}}$BS总功率消耗
    ${{\boldsymbol{r}}_k}$RIS到用户k的信道${P_c}$系统总电路损耗
    $ {\theta _n} $反射系数的相移${{\hat {\boldsymbol G}}}$BS到RIS的估计信道
    ${t_k}$,${\mu _k}$松弛变量$\Delta {\boldsymbol{G}}$BS到RIS的估计信道误差
    ${\boldsymbol{\theta }}$相移对角矩阵${{{\hat {\boldsymbol r}}}_k}$RIS到用户k的估计信道
    $ \gamma _k^{{\text{tar}}} $用户k的目标SINR$\Delta {{\boldsymbol{r}}_k}$RIS到用户k的估计信道误差
    ${P_0}$BS的最大传输功率$ {{\boldsymbol{I}}_a} $大小为$a \times a$的单位矩阵
    下载: 导出CSV

    表  2  基于交替迭代的能效资源分配算法

     初始化系统参数$i = 1$;给定${{\boldsymbol{\varTheta }}^{(0)}}$, $\beta _k^{(0)}$, ${\lambda ^{(0)}}$, ${\boldsymbol{W}}_k^{(0)}$;最大迭
     代次数${i_{\max }}$;收敛精度$\varepsilon {\text{ = }}{10^{ - 6}}$;
     (1) for $i = 1,2,\cdots$ do
     (2)   根据给定的${ {\boldsymbol{\varTheta } }^{(i{{ - } }1)} }$和$\beta _k^{(i{{ - } }1)}$求解问题式(31)得到主动波
         束成形$\{ {{\boldsymbol{W}}_k}\} $以及$\mathcal{F}_i^1$;
     (3)   根据给定$\{ {\boldsymbol{W}}_k^{(i)}\} $和$ {{\boldsymbol{\varTheta }}^{(i - 1)}} $求解问题式(32)得到松弛变量
         ${\beta _k}$以及$ \mathcal{F}_i^2 $;
     (4)   根据给定$\{ {{\boldsymbol{W}}_k}\} $和${\beta _k}$求解问题式(33)得到被动波束成形${\boldsymbol{\varTheta }}$;
     (5) 计算$ ℱ{i}_{}({\lambda }^{(i)}) $, $\lambda _{}^{(i)} = {\displaystyle\sum\nolimits_{k = 1}^K { { {\log }_2}(1 + \beta _k^{(i)})} }/\xi \displaystyle\sum\nolimits_{k = 1}^K$   ${ {\text{Tr} }\left( { {\boldsymbol{W} }_k^{(i)} } \right)} +{P_c}$;

     (6)   if $ \left\{ {\begin{array}{*{20}{c}} {{{\left| {\mathcal{F}_i^1 - \mathcal{F}_{i - 1}^1} \right|}/ {\mathcal{F}_{i - 1}^1 \le \varepsilon }}} \\ \begin{gathered} {{\left| {\mathcal{F}_i^2 - \mathcal{F}_{i - 1}^2} \right|}/ {\mathcal{F}_{i - 1}^2 \le \varepsilon }} \\ {{\left| {{\mathcal{F}_i} - {\mathcal{F}_{i - 1}}} \right|}/{{\mathcal{F}_{i - 1}} \le \varepsilon }} \\ \end{gathered} \end{array}} \right. $ 或者$i = {i_{\max }}$则

     (7)     break
     (8)  else
     (9)     $i = i + 1$;
     (10)   end if
     (11) end for
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-07
  • 修回日期:  2021-09-05
  • 网络出版日期:  2021-09-30
  • 刊出日期:  2022-07-25

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