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能量收集认知多跳中继网络中断性能分析及优化

罗轶 孔静恬 董健 佘青青 黄慧 黄正宇

罗轶, 孔静恬, 董健, 佘青青, 黄慧, 黄正宇. 能量收集认知多跳中继网络中断性能分析及优化[J]. 电子与信息学报, 2021, 43(10): 2920-2927. doi: 10.11999/JEIT200702
引用本文: 罗轶, 孔静恬, 董健, 佘青青, 黄慧, 黄正宇. 能量收集认知多跳中继网络中断性能分析及优化[J]. 电子与信息学报, 2021, 43(10): 2920-2927. doi: 10.11999/JEIT200702
Yi LUO, Jingtian KONG, Jian DONG, Qingqing SHE, Hui HUANG, Zhengyu HUANG. Outage Performance Analysis and Optimization of Energy Harvesting Cognitive Multihop Relay Networks[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2920-2927. doi: 10.11999/JEIT200702
Citation: Yi LUO, Jingtian KONG, Jian DONG, Qingqing SHE, Hui HUANG, Zhengyu HUANG. Outage Performance Analysis and Optimization of Energy Harvesting Cognitive Multihop Relay Networks[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2920-2927. doi: 10.11999/JEIT200702

能量收集认知多跳中继网络中断性能分析及优化

doi: 10.11999/JEIT200702
基金项目: 国家自然科学基金(61971450),湖南省科技计划项目(2018TP1018),湖南省自然科学基金(2018JJ2533)
详细信息
    作者简介:

    罗轶:男,1980年生,博士,讲师,研究方向为认知无线通信与物联网关键技术等

    董健:男,1980年生,博士,教授,博士生导师,研究方向为认知协作通信和5G/物联网通信

    黄慧:女,1997年生,硕士生,研究方向为无线体域网MAC层协议设计

    通讯作者:

    董健 dong0531@126.com

  • 中图分类号: TN925

Outage Performance Analysis and Optimization of Energy Harvesting Cognitive Multihop Relay Networks

Funds: The National Natural Science Foundation of China (61971450), The Hunan Provincial Science and Technology Project Foundation (2018TP1018), The Natural Science Foundation of Hunan Province (2018JJ2533)
  • 摘要: 针对能量收集认知无线网络中的多跳中继传输问题,该文构建了一种新的具有主网络干扰的功率信标(PB)辅助能量收集认知多跳中继网络模型,并提出单向传输方案。在干扰链路统计信道状态信息场景下,推导了次网络精确和渐近总中断概率闭合式。针对精确总中断概率表达式的复杂性和非凸性,采用自适应混沌粒子群优化(ACPSO)算法对次网络总中断性能进行优化。仿真结果表明,PB功率、干扰约束、次网络跳数、能量收集比率、主接收端数目和信道容量阈值等参数对中断性能影响显著,所提算法能快速和有效地对网络中断性能进行优化。
  • 图  1  网络模型

    图  2  SN功率中断概率与${P_{\rm{t}}}$间关系

    图  3  SN信道中断概率与${P_{\rm{t}}}$间关系

    图  4  SN总中断概率与${P_{\rm{t}}}$间关系

    图  5  SN总中断概率与${P_{\rm{I}} }$间关系

    图  6  SN总中断概率与${P_0}$间关系

    图  7  SN总中断概率与$\alpha $间关系

    图  8  优化算法下SN总中断概率与PI间关系

    图  9  总中断概率适应度与迭代次数间关系

    表  1  ACPSO算法

     输入:${\lambda _k}$, ${\beta _k}$, ${\omega _k}$, ${\theta _k}$, ${P_{\rm{I}} }$, ${P_0}$, ${P_{\max }}$, $N$, $K$, ${R_{\rm{th}}}$, $\varepsilon $, $\eta $, $\zeta $, $S$, $l$和$L$;
     输出:全局最优值$P_{\rm{t}}^ * $和${\alpha ^ * }$;
     (1)设置算法参数,采用式(29)分别对粒子s, $s = 1,2, \cdots ,S$的位置和速度进行初始化。其中,$t{\rm{ = }}1$, ${x_{s,1}}\left( t \right) \in \left[ {0,{P_{\max }}} \right]$, ${x_{s,2}}\left( t \right) \in \left( {0,1} \right]$,   ${v_{s,1}}\left( t \right) \in \left[ { - \mu {P_{\max }},\mu {P_{\max }}} \right]$, ${v_{s,2}}\left( t \right) \in \left[ { - \mu ,\mu } \right]$, $\mu $为0~1之间均匀分布的随机数,也由式(29)产生;
     (2)将${x_{s,1}}\left( 1 \right)$和${x_{s,2}}\left( 1 \right)$代入式(23),计算${f_s}\left( 1 \right)$。令${p_{s,1}}{\rm{ = }}{x_{s,1}}\left( 1 \right)$, ${p_{s,2}}{\rm{ = }}{x_{s,2}}\left( 1 \right)$, ${p_{g,1}} = {x_{{s^*},1}}\left( 1 \right)$, ${p_{g,2}} = {x_{{s^*},2}}\left( 1 \right)$, ${s^*}$为${f_s}\left( 1 \right)$中最小值  所对应的粒子;
     (3)依据式(26)调整${\omega _s}\left( t \right)$;依据式(27)和式(28)分别调整${c_1}\left( t \right)$和${c_2}\left( t \right)$;依据式(29)调整${r_1}\left( t \right)$和${r_2}\left( t \right)$, $t \ge 1$;
     (4)依据式(24)和式(25)更新${v_{s,1}}\left( t \right){\rm{ = }}\min \left( {\max \left( { - \mu {P_{\max }},{v_{s,1}}\left( t \right)} \right),\mu {P_{\max }}} \right)$和${v_{s,2}}\left( t \right){\rm{ = }}\min \left( {\max \left( { - \mu ,{v_{s,2}}\left( t \right)} \right),\mu } \right)$以及  ${x_{s,1}}\left( t \right){\rm{ = }}\min \left( {\max \left( {0,{x_{s,1}}\left( t \right)} \right),{P_{\max }}} \right)$和${x_{s,2}}\left( t \right){\rm{ = }}\min \left( {\max \left( {0,{x_{s,2}}\left( t \right)} \right),1} \right)$, $t \ge2$;根据式(23)和式(30)分别计算${f_s}\left( t \right)$和$\varphi \left( t \right)$, $t \ge 2$,  计算更新${p_{s,1}}$, ${p_{s,2}}$, ${p_{g,1}}$和${p_{g,2}}$;
     (5)若满足$t > L$或$\varphi \left( t \right) < {10^{ - 6}}$,则停止迭代,输出$P_{\rm{t}}^ * = {p_{g,1}}$和${\alpha ^ * }{\rm{ = }}{p_{g,2}}$,否则返回步骤(3),继续迭代。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-10
  • 修回日期:  2021-02-05
  • 网络出版日期:  2021-03-22
  • 刊出日期:  2021-10-18

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