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基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法

柴蓉 王令 陈明龙 陈前斌

柴蓉, 王令, 陈明龙, 陈前斌. 基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法[J]. 电子与信息学报, 2019, 41(11): 2565-2570. doi: 10.11999/JEIT180408
引用本文: 柴蓉, 王令, 陈明龙, 陈前斌. 基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法[J]. 电子与信息学报, 2019, 41(11): 2565-2570. doi: 10.11999/JEIT180408
Rong CHAI, Ling WANG, Minglong CHEN, Qianbin CHEN. Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2565-2570. doi: 10.11999/JEIT180408
Citation: Rong CHAI, Ling WANG, Minglong CHEN, Qianbin CHEN. Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2565-2570. doi: 10.11999/JEIT180408

基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法

doi: 10.11999/JEIT180408
基金项目: 国家自然科学基金(61571073),国家科技重大专项(2016ZX03001010-004)
详细信息
    作者简介:

    柴蓉:女,1974年生,教授,研究方向为通信网络架构及关键技术、无线资源管理及移动性管理技术等

    王令:女,1993年生,硕士生,研究方向为无线通信、无线资源管理及网络虚拟化等

    陈明龙:男,1993年生,硕士生,研究方向为无线资源管理、网络虚拟化及内容缓存等

    陈前斌:男,1967年生,教授,研究方向为无线通信、通信网络理论、软件定义网络、多媒体技术等

    通讯作者:

    柴蓉 chairong@cqupt.edu.cn

  • 中图分类号: TN915

Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization

Funds: The National Science Foundation of China (61571073), The National Science and Technology Specific Project of China (2016ZX03001010-004)
  • 摘要: 针对蜂窝网络传输性能及基站(BS)缓存能力受限,多用户内容请求难以满足用户服务质量(QoS)需求等问题,该文提出一种蜂窝终端直通(D2D)通信联合用户关联及内容部署算法。考虑到位于特定区域的多用户可能对于相同内容存在内容请求,该文引入成簇思想,提出一种成簇及内容部署机制,通过为各簇头推送热点内容,而簇成员基于D2D通信模式关联簇头获取所需内容,可实现高效内容获取。综合考虑成簇数量、用户关联簇头、簇头缓存容量及传输速率等限制条件,建立基于用户总业务时延最小化的联合成簇及内容部署优化模型。该优化问题是一个非凸的混合整数优化问题,该文运用拉格朗日部分松弛法,将原优化问题等价转换为3个凸优化的子问题,并基于迭代算法及Kuhn-Munkres算法联合求解各子问题,从而得到联合成簇及内容部署优化策略。最后通过MATLAB仿真验证所提算法的有效性。
  • 图  1  蜂窝D2D通信系统场景图

    图  2  基于用户成簇机制的蜂窝D2D系统场景图

    图  3  总业务时延与迭代次数关系图

    图  4  总业务时延与业务到达速率关系图(不同服务速率)

    图  5  总业务时延与业务到达速率关系图(不同最低传输速率)

    图  6  总业务时延与子信道带宽关系图(不同噪声功率谱密度)

    图  7  总业务时延与子信道带宽关系图(不同业务到达速率)

    表  1  联合用户关联及内容部署算法

     (1) 确定L种簇头组合策略;
     (2) for l=1,针对第l种簇头组合策略;
     (3) 设置最大迭代次数T max和最大容忍值ε
     (4) 初始化拉格朗日因子ηi,j,k,φi,j,k,θi,j,k
     (5) 重复主程序;
     (6) 求解用户关联子问题得到局部变量值δi,j
      求解内容部署子问题得到局部变量值βj,k
      求解联合优化子问题得到局部变量值αi,j,k
     (7) 更新拉格朗日因子;
      ηi,j,k(t+1)=[ηi,j,k(t)ω1(αi,j,k(t)+1δi,j(t)βj,k(t))]+,
      φi,j,k(t+1)=[φi,j,k(t)ω2(δi,j(t)αi,j,k(t))]+,
      θi,j,k(t+1)=[θi,j,k(t)ω3(βj,k(t)αi,j,k(t))]+;
     (8) 若Mi=1Mj=1Kk=1[|ηi,j,k(t+1)ηi,j,k(t)|     +|φi,j,k(t+1)φi,j,k(t)|+|θi,j,k(t+1)θi,j,k(t)|]ε
     (9) 算法收敛;
      返回 δ(l)i,j=δi,j,β(l)j,k=βj,k,α(l)i,j,k=αi,j,k;
     (10) 否则 t=t+1
     (11) 重复步骤(6)—步骤(10),直到算法收敛或t=T max
     (12) l=l+1,重复步骤(5)—步骤(11),得到δ(l)i,j,β(l)j,k,α(l)i,j,kD(l),直至l=L
     (13) 比较L种簇头组合下的最优业务时延,选择最优用户关联及内容部署优化策略,即{δ(l)i,j,β(l)j,k,α(l)i,j,k}=argminD(l)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-02
  • 修回日期:  2019-05-21
  • 网络出版日期:  2019-07-19
  • 刊出日期:  2019-11-01

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