Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method
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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。Abstract: Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) is able to create an all-space intelligent radio environment to effectively improve the performance of wireless communication systems, thus it has vast research potential. Therefore, in this paper, a large-scale STAR-RIS-assisted near-field Integrated Sensing and Communication (ISAC) approach is proposed. Cramér-Rao Bound (CRB) of the three-dimensional estimation of the sensing target is optimized. First, the near-field system model is built and then beam steering vectors between base station, STAR-RIS, communication users, sensing target and sensor are derived respectively. Second, the sensing performance is optimized by designing the transmit beamforming matrix, the covariance matrix of transmit signal and the STAR-RIS coefficients. Third, a non-convex optimization problem is solved via semi-definite relaxation approach. The simulation results show the effectiveness of our proposed ISAC approach, and the positioning performance advantage brought by the extra distance freedom of near field.
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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 
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