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基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法

李国权 徐勇军 陈前斌

李国权, 徐勇军, 陈前斌. 基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法[J]. 电子与信息学报, 2020, 42(4): 957-964. doi: 10.11999/JEIT190419
引用本文: 李国权, 徐勇军, 陈前斌. 基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法[J]. 电子与信息学报, 2020, 42(4): 957-964. doi: 10.11999/JEIT190419
Guoquan LI, Yongjun XU, Qianbin CHEN. Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks[J]. Journal of Electronics and Information Technology, 2020, 42(4): 957-964. doi: 10.11999/JEIT190419
Citation: Guoquan LI, Yongjun XU, Qianbin CHEN. Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks[J]. Journal of Electronics and Information Technology, 2020, 42(4): 957-964. doi: 10.11999/JEIT190419

基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法

doi: 10.11999/JEIT190419
基金项目: 国家自然科学基金(61601071),重庆市教委科学技术研究计划项目(KJQN201800606),重庆市基础研究与前沿探索专项面上项目(cstc2019jcyj -msxmX0666, cstc2019jcyj-xfkxX0002), 重庆市科技创新领军人才项目(CSTCCXLJRC201908),山东省无线通信技术重点实验室开放课题(SDKLWCT-2019-04)
详细信息
    作者简介:

    李国权:男,1980年生,副教授,硕士生导师,研究方向为多蜂窝网络性能分析

    徐勇军:男,1986年生,副教授,硕士生导师,研究方向为异构无线网络资源分配

    陈前斌:男,1967年生,教授,博士生导师,研究方向为下一代移动通信

    通讯作者:

    徐勇军 xuyj@cqupt.edu.cn

  • 中图分类号: TN929.5

Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks

Funds: The National Natural Science Foundation of China (61601071), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800606), The Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0666, cstc2019jcyj-xfkxX0002), The Chongqing Science and Technology Innovation Leading Talent Support Program (CSTCCXLJRC201908), The Open Research Fund from Shandong Provincial Key Laboratory of Wireless Communication Technologies (SDKLWCT-2019-04)
  • 摘要:

    针对多蜂窝多用户异构无线网络干扰管理和效率提升问题,该文研究了基于干扰效率最大的下行链路基站(BS)-用户匹配和功率分配问题。首先,考虑宏用户和微蜂窝用户的服务质量,将问题建模为多变量混合整数非线性规划问题。其次将原问题分解为基站选择和功率分配两个子问题。针对基站选择问题,利用凸优化问题获得最优基站选择策略;针对功率分配问题,利用二次变换法和Dinkelbach辅助变量法,将功率分配问题转换为凸优化问题求解。仿真结果表明,与现有算法对比,该算法具有较好的干扰效率和干扰控制性能。

  • 图  1  多蜂窝异构无线网络下行系统模型

    图  2  算法收敛性能图

    图  3  最大干扰门限对干扰效率的影响

    图  4  最大干扰门限对能量效率的影响

    图  5  发射功率门限对干扰效率的影响

    图  6  发射功率门限对能量效率的影响

    图  7  发射功率门限对宏用户接收机处干扰的影响

    表  1  基站-用户匹配选择算法

     初始化微蜂窝网络能服务的最大用户数${K^n}$,最小用户速率需求门限$R_{n,k}^{\min }$和发射功率${p_n}(t) = {p_0}$;
     初始化拉格朗日乘子${\beta _k}(0) = {\beta _{k,0}}$, ${\chi _n}(0) = {\chi _{n,0}}$和${\lambda _{n,k}}(0) = {\lambda _{n,k,0}}$;初始化网络用户数量和基站用户数$M,N$和$K$;初始化
     步长${s_1}(t),{s_2}(t)$和${s_3}(t)$。初始化第$n$个微蜂窝所接入用户数量集合为${U_n} = \varnothing $, $\left| {{U_n}} \right|$为集合中有多少个元素。
     While $t \le {T^{\max } }$或者${\left\| { {\varphi }(t + 1) - {\varphi }(t)} \right\|_2} \le \varepsilon $;其中${T^{\max }}$为最大迭代次数;$\varepsilon $为拉格朗日乘子收敛精度;${\varphi }(t) = {[{\beta _k}(t),{\chi _n}(t),{\lambda _{n,k} }(t)]^{\rm{T} } }$。
     For k=1:1:K
       For n=1:1:N
        if $\left| { {U_n} } \right| \le {K^n}$
       根据式(9)计算${n^*}$,从而根据式(8)计算${\alpha _{n,k}}$;根据式(10)—式(12)更新拉格朗日乘子。
       Else
         Break;
       End if
       End For
      将用户编号$k$存储在${U_n}$中。
     End For
     End while
    下载: 导出CSV

    表  2  最优功率分配算法

     初始化微蜂窝网络能服务的最大用户数${K^n}$,最小用户速率需求门限$R_{n,k}^{\min }$和发射功率${p_n}(t) = {p_0}$;
     初始化拉格朗日乘子,网络用户数量和基站用户数,初始化步长和干扰效率。
     While $j \le J$ 或者$\left| {\dfrac{ {\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{k = 1}^K { {\alpha _{n,k} }{R_{n,k} }(j)} } } }{ {\displaystyle\sum\nolimits_{m = 1}^M {\displaystyle\sum\nolimits_{n = 1}^N { {p_n}(j){h_{n,m} } } } } } - \eta (j - 1)} \right| > \varepsilon $;其中${T^{\max }}$为最大迭代次数;$\varepsilon $为收敛精度;
     For m=1:1:M
       For k=1:1:K
         For n=1:1:N
           根据式(21)、式(22)计算变量${x_{n,k}}$和最优功率${p_n}$;
           根据式(23)—式(25)更新拉格朗日乘子${\theta _n},{\mu _m},\lambda _{n,m}^{{p} }$。
         End For
       End For
     End For
     Until $t = {T_{\max }}$或收敛。
    更新 $j = j + 1$和$\eta (j) = \frac{ {\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{k = 1}^K { {\alpha _{n,k} }{R_{n,k} }(j - 1)} } } }{ {\displaystyle\sum\nolimits_{m = 1}^M {\sum\nolimits_{n = 1}^N { {p_n}(j - 1){h_{n,m} } } } } }$。
    End while
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-07
  • 修回日期:  2020-01-18
  • 网络出版日期:  2020-02-20
  • 刊出日期:  2020-06-04

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