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基于用户窃听的MU-MISO反向散射通信系统鲁棒资源分配算法

徐勇军 徐然 周继华 陈量 黄东

徐勇军, 徐然, 周继华, 陈量, 黄东. 基于用户窃听的MU-MISO反向散射通信系统鲁棒资源分配算法[J]. 电子与信息学报, 2024, 46(1): 204-212. doi: 10.11999/JEIT221508
引用本文: 徐勇军, 徐然, 周继华, 陈量, 黄东. 基于用户窃听的MU-MISO反向散射通信系统鲁棒资源分配算法[J]. 电子与信息学报, 2024, 46(1): 204-212. doi: 10.11999/JEIT221508
XU Yongjun, XU Ran, ZHOU Jihua, CHEN Liang, HUANG Dong. Robust Resource Allocation Algorithm in MU-MISO Backscatter Communication Systems with Eavesdroppers[J]. Journal of Electronics & Information Technology, 2024, 46(1): 204-212. doi: 10.11999/JEIT221508
Citation: XU Yongjun, XU Ran, ZHOU Jihua, CHEN Liang, HUANG Dong. Robust Resource Allocation Algorithm in MU-MISO Backscatter Communication Systems with Eavesdroppers[J]. Journal of Electronics & Information Technology, 2024, 46(1): 204-212. doi: 10.11999/JEIT221508

基于用户窃听的MU-MISO反向散射通信系统鲁棒资源分配算法

doi: 10.11999/JEIT221508
基金项目: 国家自然科学基金(62271094),重庆市教委科学技术研究项目(KJZD-K202200601),重庆市自然科学基金创新发展联合基金重点项目(CSTB2022NSCQ-LZX0009)
详细信息
    作者简介:

    徐勇军:男,副教授,博士生导师,研究方向为反向散射通信、鲁棒资源分配

    徐然:男,硕士生,研究方向为反向散射、鲁棒资源分配

    周继华:男,研究员,博士生导师,研究方向为无线网络、资源分配等

    陈量:男,正高级工程师,研究方向为软件定义网络技术等

    通讯作者:

    徐勇军 xuyj@cqupt.edu.cn

  • 中图分类号: TN929

Robust Resource Allocation Algorithm in MU-MISO Backscatter Communication Systems with Eavesdroppers

Funds: The National Natural Science Foundation of China (62271094), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The Key Fund of Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009)
  • 摘要: 针对反向散射通信系统信道估计不准、信息容易被窃听等问题,该文提出一种基于用户窃听的多用户-多输入单输出(MU-MISO)反向散射通信系统鲁棒资源分配算法,以提高系统传输鲁棒性与信息安全性。首先,考虑基站最大功率、时间分配、信道不确定性、能量收集和保密率等约束,建立一个MU-MISO的反向散射通信系统鲁棒资源分配问题。其次,基于非线性能量收集模型和有界球形信道不确定性模型,利用变量松弛法和S过程将原NP-hard问题转化为确定性问题,随后利用连续凸近似、半正定松弛与块坐标下降法将其转化为凸优化问题求解。仿真结果表明,与传统非鲁棒算法对比,所提算法具有较高的系统容量和较低的中断概率。
  • 图  1  反向散射通信物理层安全网络

    图  2  本文算法收敛性能

    图  3  最大发射功率和反射系数对吞吐量的影响

    图  4  信道不确定性与噪声功率对保密率的影响

    图  5  反射系数与信道不确定性对保密吞吐量的影响

    图  6  信道不确定性与反射系数对保密吞吐量的影响

    图  7  信道不确定性对中断概率的影响

    算法1 基于块坐标下降法的鲁棒资源分配迭代算法
     1. 初始化系统参数:$ K,\,M,\,{\beta _k},\,E_k^{\text{C}},\,\sigma _k^2,\,\sigma _e^2,\,T,\,\bar x_k^2,\,\bar x_k^3,\,\bar y_k^1,\,\bar \gamma _k^{\text{E}},\,\alpha _k^{\left( 0 \right)},\,{R^{{\text{sum}}(0)}} $;阈值:$r_k^{\min },\,{P^{\max }}$;估计误差上界:$\varepsilon $;收敛精度:$ \varpi $;
     初始化迭代次数:$l{\text{ = }}1$。
     2. Repeat
     3.  固定$ \alpha _k^{\left( 0 \right)} $,求解子问题1,获得$ {\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }} $。
         if $ {\boldsymbol{W}}_k^{\left( l \right) * } $满足$ \text{Rank}\left({{\boldsymbol{W}}}_{k}^{\left(l\right)\ast }\right)=1 $可以通过特征值分解获得最优波束向量,即${\boldsymbol{W}}_k^{\left( l \right) * }{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * }{\boldsymbol{w}}_k^{\left( l \right) * {\text{H}}}$。
         else if ${\boldsymbol{W}}_k^{\left( l \right) * }$的秩大于1,可以通过高斯随机化获得最优向量。
     4.  固定${\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }}$求解子问题2,获得$ \alpha _k^{\left( l \right) * } $。
     5.  计算$ {R^{{\text{sum}}(l)}} $,并且更新迭代次数$l = l + 1$。
     6. Until $ \left| {{R^{{\text{sum}}(l)}} - {R^{{\text{sum}}(l - 1)}}} \right| \le \varpi $。
     7. Return $ {\boldsymbol{w}}_k^{{\text{opt}}}{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{{\text{opt}}}} = {{\boldsymbol{Z}}^{\left( l \right) * }},\,\alpha _k^{{\text{opt}}} = \alpha _k^{\left( l \right) * },\,{R^{{\text{sum}}}} = {R^{{\text{sum}}(l)}} $。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-05
  • 修回日期:  2023-09-24
  • 网络出版日期:  2023-10-18
  • 刊出日期:  2024-01-17

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