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基于连续凸逼近的协作式非正交多址接入联合无线携能通信的能效优化方案

冯熳 胡忠颖 巴特尔

冯熳, 胡忠颖, 巴特尔. 基于连续凸逼近的协作式非正交多址接入联合无线携能通信的能效优化方案[J]. 电子与信息学报, 2023, 45(4): 1147-1153. doi: 10.11999/JEIT220170
引用本文: 冯熳, 胡忠颖, 巴特尔. 基于连续凸逼近的协作式非正交多址接入联合无线携能通信的能效优化方案[J]. 电子与信息学报, 2023, 45(4): 1147-1153. doi: 10.11999/JEIT220170
FENG Man, HU Zhongying, Bateer. Energy Efficiency Optimization Algorithm of Cooperative Non-Orthogonal Multiple Access joint Simultaneous Wireless Information and Power Transfer Based on Successive Convex Approximation[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1147-1153. doi: 10.11999/JEIT220170
Citation: FENG Man, HU Zhongying, Bateer. Energy Efficiency Optimization Algorithm of Cooperative Non-Orthogonal Multiple Access joint Simultaneous Wireless Information and Power Transfer Based on Successive Convex Approximation[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1147-1153. doi: 10.11999/JEIT220170

基于连续凸逼近的协作式非正交多址接入联合无线携能通信的能效优化方案

doi: 10.11999/JEIT220170
详细信息
    作者简介:

    冯熳:女,副教授,研究方向为超窄带通信、军事抗干扰通信

    胡忠颖:女,硕士生,研究方向为非正交多址接入、无线携能通信

    巴特尔:男,副研究员,研究方向为5G非地面网络、卫星通信

    通讯作者:

    冯熳 fengman@seu.edu.cn

  • 中图分类号: TN914.5

Energy Efficiency Optimization Algorithm of Cooperative Non-Orthogonal Multiple Access joint Simultaneous Wireless Information and Power Transfer Based on Successive Convex Approximation

  • 摘要: 在传统的非正交多址(NOMA)系统中,通常将更多的功率分配给边缘用户以此来保证其通信质量,系统公平性以牺牲系统容量为代价。基于协作通信的NOMA系统虽可解决上述问题,但在协作阶段中心用户需承担中继的作用,这种方式必将给中心用户带来一定的负担。为了兼顾系统容量和公平性,该文提出一种基于协作通信和无线携能通信(SWIPT)的新型资源分配方案,该方案在满足边缘用户通信质量情况下,使用能量收集设备完成能量收集,通过连续凸逼近(SCA)求解目标问题最大化系统能效。仿真结果表明,与传统NOMA和协作式非正交多址接入系统(CNOMA)相比,CNOMA-SWIPT系统的能量效率得到了较大的提高,在基站最大发射功率为30 dBm时相比NOMA系统能达到60.8%的增益,相比CNOMA系统能达到比CNOMA系统高出约11.5%的增益,更符合绿色通信的发展理念。
  • 图  1  CNOMA-SWIPT传输示意图

    图  2  基于PS的SWIPT策略的原理

    图  3  中断性能对比

    图  4  两种资源分配方式下的能效对比图

    图  5  CNOMA-SWIPT和传统NOMA能效对比

    算法1 Dinkelbach分数规划
     初始化:迭代次数n=0,准许误差$\epsilon={10}^{-4}$, $ {\lambda }^{n}=0 $
     若F($ \lambda $)=$ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–${\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m} }\ge \epsilon$,则执行:
     用$ {\lambda }^{n} $求解max ($ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–$ {\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m}} $) ;
     更新$ {R}_{\mathrm{s}\mathrm{u}\mathrm{m}} $–$ {\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m}} $, $ {\lambda }^{n} $, n=n+1;
     否则:$ {{\eta }_{\mathrm{E}\mathrm{E}}}^{*}={\lambda }^{n} $
    下载: 导出CSV
    算法2 基于SCA的P2求解算法
     初始化:迭代次数j=1,${a}_{i} ^{j} {,b}_{i} ^{j}$=0(i=1,2,3),$\varDelta $=1,准许误差
         $ \epsilon={10}^{-4} $
     若$\varDelta\ge \epsilon$,则执行:
        通过CVX工具箱求解P2,得到${q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}$
          更新${z}_{i} ^{j} {,b}_{i} ^{j}$,${a}_{i} ^{j} {,b}_{i} ^{j}$
         更新:$\varDelta=|{F\left(\lambda \right)}^{j}-{F\left(\lambda \right)}^{j-1}|$;
         j=j+1;
     否则: 输出$\lambda =\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }({ {q}_{1} }^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$
    下载: 导出CSV
    算法3 基于SCA的CNOMA-SWIPT系统能效优化算法
     初始化:迭代次数n=0,准许误差$ \epsilon={10}^{-4} $, $ {\lambda }^{n}=0 $
     若F($ {\lambda }^{n} $)$ \ge \epsilon $,则执行:
        设j=0, ${a}_{i}^{j} {,b}_{i} ^{j}$=0(i=1,2,3),$\varDelta=1 $;
        若$\varDelta\ge \epsilon$,则执行:
          通过CVX工具箱求解P2,得到${q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}$;
         更新${z}_{i} ^{j} {,b}_{i} ^{j}$, ${a}_{i} ^{j} {,b}_{i} ^{j}$, $\varDelta=|{F\left(\lambda \right)}^{j}-{F\left(\lambda \right)}^{j-1}|$;
         j=j +1;
       直到$\varDelta < \epsilon$,执行:
         F($ {\lambda }^{n} $)=${R}_{\mathrm{s}\mathrm{u}\mathrm{m} }\left( {q}_{1} ^{j}, {q}_{2} ^{j} {q}_{3} ^{j},{ {q}_{4} }^{j}, {q}_{5} ^{j}\right)-{\lambda }^{n}{P}_{\mathrm{s}\mathrm{u}\mathrm{m} }\left( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j}\right); {\lambda }^{n}=\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$
          n=n+1;
     若F($ {\lambda }^{n} $)<$ \epsilon $: 输出$\lambda =\dfrac{ {R}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}{ {P}_{\mathrm{s}\mathrm{u}\mathrm{m} }( {q}_{1} ^{j}, {q}_{2} ^{j}, {q}_{3} ^{j}, {q}_{4} ^{j}, {q}_{5} ^{j})}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-22
  • 修回日期:  2020-10-30
  • 网络出版日期:  2022-11-01
  • 刊出日期:  2023-04-10

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