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大规模STAR-RIS辅助的近场ISAC传输方法

王小明 李佳琪 刘婷 蒋锐 徐友云

王小明, 李佳琪, 刘婷, 蒋锐, 徐友云. 大规模STAR-RIS辅助的近场ISAC传输方法[J]. 电子与信息学报. doi: 10.11999/JEIT240018
引用本文: 王小明, 李佳琪, 刘婷, 蒋锐, 徐友云. 大规模STAR-RIS辅助的近场ISAC传输方法[J]. 电子与信息学报. doi: 10.11999/JEIT240018
WANG Xiaoming, LI Jiaqi, LIU Ting, JIANG Rui, XU Youyun. Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240018
Citation: WANG Xiaoming, LI Jiaqi, LIU Ting, JIANG Rui, XU Youyun. Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240018

大规模STAR-RIS辅助的近场ISAC传输方法

doi: 10.11999/JEIT240018
基金项目: 国家自然科学基金 (62101274, 62371246)
详细信息
    作者简介:

    王小明:男,副教授,研究方向为无线移动通信

    李佳琪:女,硕士生,研究方向为通信感知一体化

    刘婷:女,副教授,研究方向为无线移动通信

    蒋锐:男,副教授,研究方向为无线移动通信

    徐友云:男,教授,研究方向为无线移动通信

    通讯作者:

    李佳琪 1222014134@njupt.edu.cn

  • 中图分类号: TN929.5

Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method

Funds: The National Natural Science Foundation of China (62101274, 62371246)
  • 摘要: 同时透射和反射可重构智能表面(STAR-RIS)能够创建全空间智能无线电环境,有效提高无线通信系统性能,具有广阔的研究潜力。因此,该文提出一种大规模STAR-RIS辅助的近场通感一体化(ISAC)方法,并对感知目标3维参数估计的克拉美罗界(CRB)进行优化。首先,搭建近场系统模型,分别推导基站、STAR-RIS、通信用户、感知目标与传感器之间的导向矢量。其次,通过设计基站发射波束成形矩阵、发射信号协方差矩阵和STAR-RIS透射反射系数,实现感知性能最优化。再次,针对非凸优化问题利用半正定松弛方法进行求解。仿真结果表明了所提出ISAC方案的有效性,以及近场额外距离自由度所带来的定位性能优势。
  • 图  1  导向矢量的推导模型

    图  2  CRB根值与通信用户SINR阈值的关系

    图  3  CRB根值与传感元件个数${N_s}$的关系

    图  4  感知目标定位的3维切片图,$\tilde \gamma = 0$

    图  5  感知目标定位的3维切片图,$\tilde \gamma {\text{ = 20}}\;{\text{dB}}$

    1  优化过程算法

     初始化参数:${\nu ^l}$, ${\eta ^l}$, ${{\boldsymbol{\varLambda}} ^l}$;设置外层收敛精度$0 < \chi < 1$,迭代
     次数初始化$m = 1$,最大迭代次数为${M_{{\text{max}}}}$;内层收敛精度
     $0 < c < 1$,迭代次数为$u$,最大迭代次数为${U_{{\text{max}}}}$,惩罚因子更
     新参数为$0 < d < 1$;初始化h$\left( {{\nu ^0}} \right)$;
     (1) While$\left| {h\left( {{\nu ^m}} \right) - h\left( {{\nu ^{m - 1}}} \right)} \right| \ge \chi $ or $m \le {M_{{\text{max}}}}$ do
     (2)  $u = 1$
     (3)  While $\left| {{\text{tr}}{{({{\boldsymbol{E}}^{ - 1}})}^u} - {\text{tr}}{{({E^{ - 1}})}^{u - 1}}} \right| \ge c$ or $u \le {U_{{\text{max}}}}$ do
     (4)   固定${\varepsilon ^u}$,求得$ \mathcal{S}\mathcal{P}1 $的最优解$\tilde \tau $并更新${\left( {\tilde \tau } \right)^u}$;
     (5)   固定${\left( {\tilde \tau } \right)^u}$,求得$ \mathcal{S}\mathcal{P}2 $的最优解$\tilde \varepsilon $并更新${\left( {\tilde \varepsilon } \right)^u}$;
     (6)   设置迭代次数$u = u + 1$;
     (7)  End while
     (8)  If $h\left( {{\nu ^m}} \right) \le 0.95h\left( {{\nu ^{m - 1}}} \right)$ then
     (9)   将${\left( {\tilde \varepsilon } \right)^u}$和${\left( {\tilde \tau } \right)^u}$代入更新${\bar {\boldsymbol{O}}^m}$,
         ${\eta ^m} = {\eta ^{m - 1}},{{\boldsymbol{\varLambda}} ^m} = {{\boldsymbol{\varLambda}} ^{m - 1}} + \dfrac{1}{\eta }{\bar {\boldsymbol{O}}^m}$;
     (10) else
     (11)   ${{\boldsymbol{\varLambda}} ^m} = {{\boldsymbol{\varLambda}} ^{m - 1}},{\eta ^m} = d{\eta ^{m - 1}}$;
     (12) 设置迭代次数$m = m + 1$,更新$h\left( {{\nu ^m}} \right) = {\left\| {{{\bar {\boldsymbol{O}}}^m}} \right\|_\infty }$;
     (13) End while
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-16
  • 修回日期:  2024-09-06
  • 网络出版日期:  2024-09-28

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