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无人机辅助的非正交多址反向散射通信系统max-min速率优化算法

王正强 胡扬 樊自甫 万晓榆 徐勇军 多滨

王正强, 胡扬, 樊自甫, 万晓榆, 徐勇军, 多滨. 无人机辅助的非正交多址反向散射通信系统max-min速率优化算法[J]. 电子与信息学报, 2023, 45(7): 2358-2365. doi: 10.11999/JEIT221210
引用本文: 王正强, 胡扬, 樊自甫, 万晓榆, 徐勇军, 多滨. 无人机辅助的非正交多址反向散射通信系统max-min速率优化算法[J]. 电子与信息学报, 2023, 45(7): 2358-2365. doi: 10.11999/JEIT221210
WANG Zhengqiang, HU Yang, FAN Zifu, WAN Xiaoyu, XU Yongjun, DUO bin. Max-min Rate Optimization Algorithm for Non-Orthogonal Multiple Access Backscatter Communication System Assisted by Unmanned Aerial Vehicles[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2358-2365. doi: 10.11999/JEIT221210
Citation: WANG Zhengqiang, HU Yang, FAN Zifu, WAN Xiaoyu, XU Yongjun, DUO bin. Max-min Rate Optimization Algorithm for Non-Orthogonal Multiple Access Backscatter Communication System Assisted by Unmanned Aerial Vehicles[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2358-2365. doi: 10.11999/JEIT221210

无人机辅助的非正交多址反向散射通信系统max-min速率优化算法

doi: 10.11999/JEIT221210
基金项目: 国家自然科学基金(61701064, 62271094),四川省区域创新合作项目(2022YFQ0017),重庆市教委科学技术研究项目(KJZD-K202200501),重庆市博士后研究项目(2021XM3082),中国博士后科学基金(2022MD723725)
详细信息
    作者简介:

    王正强:男,副教授,博士生导师,研究方向为无人机通信

    胡扬:男,硕士生,研究方向为反向散射通信

    樊自甫:男,教授,硕士生导师,研究方向为下一代无线通信

    万晓榆:男,教授,博士生导师,研究方向为下一代无线通信

    徐勇军:男,副教授,博士生导师,研究方向为反向散射通信

    多滨:男,教授,硕士生导师,研究方向为无人机通信

    通讯作者:

    王正强 wangzq@cqupt.edu.cn

  • 中图分类号: TN929.5

Max-min Rate Optimization Algorithm for Non-Orthogonal Multiple Access Backscatter Communication System Assisted by Unmanned Aerial Vehicles

Funds: The National Natural Science Foundation of China (61701064,62271094), The Sichuan Regional Innovation Cooperation Project (2022YFQ0017), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200501), Chongqing Postdoctoral Research Project (2021XM3082), China Postdoctoral Science Foundation (2022MD723725)
  • 摘要: 无人机(UAV)、非正交多址(NOMA)和反向散射通信(BC)相结合,可以满足热点地区高容量需求,提高通信质量。该文提出一种无人机辅助的NOMA反向散射通信系统最小速率最大化资源分配算法。考虑无人机发射功率、能量收集、反射系数、传输速率以及连续干扰消除(SIC)解码顺序约束,建立基于系统最小速率最大化的资源分配模型。首先利用块坐标下降将原问题分解为无人机发射功率优化、反射系数优化和无人机位置与SIC解码顺序联合优化3个子问题,然后使用反证法给出无人机最优发射功率,再用变量替换法和连续凸逼近将剩余子问题进一步转化为凸优化问题进行求解。仿真结果表明,所提算法在系统和速率与用户公平性之间具有较好折中。
  • 图  1  系统模型

    图  2  本文方案迭代图

    图  3  不同方案下系统最小速率、和速率和公平指数与无人机最大发射功率${P_{\max }}$之间的关系

    图  4  不同方案下系统最小速率、和速率和公平指数与无人机飞行高度$H$之间的关系

    图  5  不同方案下系统最小速率、和速率和公平指数与BD数目之间的关系

    算法1 最小速率最大化资源分配算法
     初始化:max-min速率$R_{\max - \min }^0$,内层迭代次数$ l = 0 $,外层迭
     代次数$ t = 0 $,惩罚参数$\mu = {\mu _0}$,步长$\gamma = {\gamma _0}$;无人机最大发射
     功率$ {P_{\max }} $,$ {{\mathbf{q}}^0},{{\mathbf{A}}^0},{{\mathbf{B}}^0},{{\mathbf{C}}^0},{{\mathbf{U}}^0},{{\mathbf{G}}^0} $;max-min速率收敛精度
     $ {\varsigma _1} $,惩罚收敛精度$ {\varsigma _2} $,外层最大迭代次数为$ {T_{\max }} $;
     (1) repeat
     (2)  repeat
     (3)    根据给定的$ {{\mathbf{q}}^l} $和$ {{\mathbf{A}}^l} $利用凸优化内点法求解问题式(9)得
          到反射系数$ {{\mathbf{R}}^*} $;
     (4)    根据$ {{\mathbf{R}}^*},{{\mathbf{B}}^l},{{\mathbf{C}}^l},{{\mathbf{U}}^l},{{\mathbf{G}}^l} $利用凸优化内点法求解问题
          式(22)得到无人机位置$ {{\mathbf{q}}^*} $和SIC解码顺序$ {{\mathbf{A}}^*} $;
     (5)    更新$ l = l + 1 $;
     (6)   until $ \left| {R_{\max - \min }^{l + 1} - R_{\max - \min }^l} \right| < {\varsigma _1} $;
     (7)    if $ \max \left\{ {{\varphi _{nm}}} \right\} \gt {\varsigma _2} $
     (8)     更新$ \mu = \gamma \mu $;
     (9)    else
     (10)     更新$ t = t + 1 $;
     (11)    end if
     (12) until $t \ge {T_{\max } }$。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-16
  • 修回日期:  2023-02-09
  • 网络出版日期:  2023-02-11
  • 刊出日期:  2023-07-10

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