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可重构智能反射面辅助认知无线电多天线安全传输方法

张军 许文婉 黄小钧

张军, 许文婉, 黄小钧. 可重构智能反射面辅助认知无线电多天线安全传输方法[J]. 电子与信息学报, 2023, 45(5): 1706-1713. doi: 10.11999/JEIT220466
引用本文: 张军, 许文婉, 黄小钧. 可重构智能反射面辅助认知无线电多天线安全传输方法[J]. 电子与信息学报, 2023, 45(5): 1706-1713. doi: 10.11999/JEIT220466
ZHANG Jun, XU Wenwan, HUANG Xiaojun. Cognitive Radio Multiple-Input Multiple-Output Wireless Secure Transmission with Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1706-1713. doi: 10.11999/JEIT220466
Citation: ZHANG Jun, XU Wenwan, HUANG Xiaojun. Cognitive Radio Multiple-Input Multiple-Output Wireless Secure Transmission with Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1706-1713. doi: 10.11999/JEIT220466

可重构智能反射面辅助认知无线电多天线安全传输方法

doi: 10.11999/JEIT220466
基金项目: 国家自然科学基金(62071247)
详细信息
    作者简介:

    张军:男,博士,博士生导师,教授,研究方向为下一代移动通信理论与关键技术、大规模MIMO、无人机通信、人工智能通信、毫米波通信、RIS辅助通信、物理层安全等

    许文婉:女,硕士生,研究方向为可重构智能反射面、认知无线电、大规模MIMO系统、物理层安全

    黄小钧:女,硕士生,研究方向为可重构智能反射面、物理层安全

    通讯作者:

    张军 zhangjun@njupt.edu.cn

  • 中图分类号: TN92

Cognitive Radio Multiple-Input Multiple-Output Wireless Secure Transmission with Reconfigurable Intelligent Surface

Funds: The National Natural Science Foundation of China (62071247)
  • 摘要: 考虑一个可重构智能反射面(RIS)辅助的频谱共享认知无线电(CR)多输入多输出(MIMO)安全通信系统。在存在窃听者的情况下,配备有多根天线的次级发送机与次级用户进行通信。首先,利用统计信道状态信息,得到了系统遍历安全速率的确定性等价表达式。然后,在满足总发送功率约束和干扰功率约束的条件下,提出一种结合泰勒级数展开法和拉格朗日乘子法的交替优化算法,联合优化了发送协方差矩阵和RIS相移矩阵。最后,仿真结果验证了所提算法的有效性。
  • 图  1  RIS辅助频谱共享CR MIMO无线通信系统

    图  2  遍历安全速率及其解析解与SNR的关系

    图  3  所提AO算法的收敛性

    图  4  遍历安全速率及其理论分析值与窃听者天线数量的关系

    图  5  遍历安全速率及其解析解与RIS反射单元数的关系

    图  6  遍历安全速率与SNR关于均方根角度扩展的关系

    算法1 AO算法
     初始化:
     $ {{\boldsymbol{Q}}^{\left( 0 \right)}} = {{\boldsymbol{I}}_M} $, $ {{\boldsymbol{\varPhi }}^{\left( 0 \right)}} = {{\boldsymbol{\varPhi }}^0} $, $ b_i^{\left( 0 \right)}{\text{ = }}\tilde b_i^{\left( 0 \right)} = e_i^{\left( 0 \right)} = \tilde e_i^{\left( 0 \right)} = $$1\;\left( {i = 1,2} \right) $,
     $ {\bar C_{\text{s}}}^{\left( 1 \right)} = 0 $, $ \varepsilon = {10^{{{ - 4}}}} $
     (1) 循环
     (2) 过程1:给定$ {\boldsymbol{\varPhi }} $,注水算法求解最优解$ {{\boldsymbol{Q}}^{{\text{opt}}}} $
     (3) 根据式(11a)和式(16),计算$ {{\boldsymbol{F}}_{\text{u}}}^{\left( {t + 1} \right)} $,$ {{\boldsymbol{F}}_{\text{e}}}^{\left( {t + 1} \right)} $和$ {{\boldsymbol{K}}^{\left( {t + 1} \right)}} $;
     (4) 根据式(15)计算$ {{\boldsymbol{Q}}^{\left( {t + 1} \right)}} $,并且根据干扰功率约束
       $ {\text{E}}\{ {\text{tr}}({{\boldsymbol{H}}_{\text{P}}}{\boldsymbol{\varPhi }}{{\boldsymbol{H}}_{\text{a}}}{\boldsymbol{Q}}{({{\boldsymbol{H}}_{\text{P}}}{\boldsymbol{\varPhi }}{{\boldsymbol{H}}_{\text{a}}})^{\text{H}}})\} \le M{P_{\text{I}}} $和发送功率约束
       $ {\text{ tr(}}{\boldsymbol{Q}}) \le M{P_{\text{T}}} $确定合适的$ {\lambda ^{\left( {t + 1} \right)}} $,$ {\nu ^{\left( {t + 1} \right)}} $;
     (5) 根据式(9)、式(10)计算$b_i^{\left( {t + 1} \right)},\tilde b_i^{\left( {t + 1} \right)},e_i^{\left( {t + 1} \right)},\tilde e_i^{\left( {t + 1} \right)} $
       $\left( {i = 1,2} \right)$,更新$ {{\boldsymbol{\tilde Q}}^{\left( {t + 1} \right)}} = {{\boldsymbol{Q}}^{\left( {t + 1} \right)}} $;
     (6) 根据式(8)计算$ {\bar C_{\text{s}}}\left( {{{\boldsymbol{Q}}^{\left( {t + 1} \right)}},{\boldsymbol{\varPhi }}} \right) $;
     (7) 更新$ t: = t + 1 $,直到$ \left| {{{\bar C}_{\text{s}}}({{\boldsymbol{Q}}^{\left( {t + 1} \right)}},{\boldsymbol{\varPhi }}) - {{\bar C}_{\text{s}}}({{\boldsymbol{Q}}^{\left( t \right)}},{\boldsymbol{\varPhi }})} \right| \le \varepsilon $;
     (8) 获得$ {{\boldsymbol{Q}}^{{\text{opt}}}} = {{\boldsymbol{Q}}^{\left( {t + 1} \right)}} $;
     (9) 过程2:给定$ {\boldsymbol{Q}} $,投影梯度上升法求解最优解$ {{\boldsymbol{\varPhi }}^{{\text{opt}}}} $
     (10) 根据式(30)更新$ {\mu ^{\left( {t + 1} \right)}} $;
     (11) 基于给定$ {{\boldsymbol{\varPhi }}^{\left( t \right)}} $,根据式(9)、式(10)计算
       $ b_i^{\left( {t + 1} \right)},\tilde b_i^{\left( {t + 1} \right)},e_i^{\left( {t + 1} \right)},\tilde e_i^{\left( {t + 1} \right)} $ $ \left( {i = 1,2} \right) $;
     (12) 根据式(28)计算$ {{\boldsymbol{\theta }}^{\left( {n + 1} \right)}} $,${ {\boldsymbol{\varPhi } }^{\left( {t + 1} \right)} } = {\rm{diag}}({ {\boldsymbol{\theta } }^{\left( {t + 1} \right)} })$;
     (13) 根据式(8)计算$ {\bar C_{\text{s}}}\left( {{\boldsymbol{Q}},{{\boldsymbol{\varPhi }}^{\left( {t + 1} \right)}}} \right) $;
     (14) 更新$ t: = t + 1 $,直到$ \left| {{{\bar C}_{\text{s}}}{\text{(}}{\boldsymbol{Q}},{{\boldsymbol{\varPhi }}^{\left( {t + 1} \right)}}{\text{)}} - {{\bar C}_{\text{s}}}({\boldsymbol{Q}},{{\boldsymbol{\varPhi }}^{\left( t \right)}})} \right| \le \varepsilon $
     (15) 获得$ {{\boldsymbol{\varPhi }}^{{\text{opt}}}} = {{\boldsymbol{\varPhi }}^{\left( {t + 1} \right)}} $;
     (16) 直到 $ \left| {{{\bar C}_{\text{s}}}\left( {{{\boldsymbol{Q}}^{{\text{opt}}}},{{\boldsymbol{\varPhi }}^{{\text{opt}}}}} \right) - {{\bar C}_{\text{s}}}\left( {{{\boldsymbol{Q}}^{\left( t \right)}},{{\boldsymbol{\varPhi }}^{\left( t \right)}}} \right)} \right| \le \varepsilon $,结束循环。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-18
  • 修回日期:  2022-10-11
  • 网络出版日期:  2022-10-13
  • 刊出日期:  2023-05-10

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