大規(guī)模STAR-RIS輔助的近場(chǎng)ISAC傳輸方法

王小明; 李佳琪; 劉婷; 蔣銳; 徐友云

doi:10.11999/JEIT240018

大規(guī)模STAR-RIS輔助的近場(chǎng)ISAC傳輸方法

doi: 10.11999/JEIT240018

1.
南京郵電大學(xué)通信與信息工程學(xué)院南京 210003
2.
南京信息工程大學(xué)人工智能學(xué)院(未來(lái)技術(shù)學(xué)院) 南京 210044

基金項(xiàng)目: 國(guó)家自然科學(xué)基金 (62101274, 62371246)

詳細(xì)信息

作者簡(jiǎn)介:
王小明：男，副教授，研究方向?yàn)闊o(wú)線(xiàn)移動(dòng)通信

李佳琪：女，碩士生，研究方向?yàn)橥ㄐ鸥兄惑w化

劉婷：女，副教授，研究方向?yàn)闊o(wú)線(xiàn)移動(dòng)通信

蔣銳：男，副教授，研究方向?yàn)闊o(wú)線(xiàn)移動(dòng)通信

徐友云：男，教授，研究方向?yàn)闊o(wú)線(xiàn)移動(dòng)通信

通訊作者:
李佳琪　1222014134@njupt.edu.cn

中圖分類(lèi)號(hào): TN929.5
計(jì)量
- 文章訪(fǎng)問(wèn)數(shù): 356
- HTML全文瀏覽量: 107
- PDF下載量: 69
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-16
- 修回日期: 2024-09-06
- 網(wǎng)絡(luò)出版日期: 2024-09-28
- 刊出日期: 2025-01-31

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

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62101274, 62371246)

摘要

摘要: 同時(shí)透射和反射可重構(gòu)智能表面(STAR-RIS)能夠創(chuàng)建全空間智能無(wú)線(xiàn)電環(huán)境，有效提高無(wú)線(xiàn)通信系統(tǒng)性能，具有廣闊的研究潛力。因此，該文提出一種大規(guī)模STAR-RIS輔助的近場(chǎng)通感一體化(ISAC)方法，并對(duì)感知目標(biāo)3維參數(shù)估計(jì)的克拉美羅界(CRB)進(jìn)行優(yōu)化。首先，搭建近場(chǎng)系統(tǒng)模型，分別推導(dǎo)基站、STAR-RIS、通信用戶(hù)、感知目標(biāo)與傳感器之間的導(dǎo)向矢量。其次，通過(guò)設(shè)計(jì)基站發(fā)射波束成形矩陣、發(fā)射信號(hào)協(xié)方差矩陣和STAR-RIS透射反射系數(shù)，實(shí)現(xiàn)感知性能最優(yōu)化。再次，針對(duì)非凸優(yōu)化問(wèn)題利用半正定松弛方法進(jìn)行求解。仿真結(jié)果表明了所提出ISAC方案的有效性，以及近場(chǎng)額外距離自由度所帶來(lái)的定位性能優(yōu)勢(shì)。
- 可重構(gòu)智能表面 /
- 通感一體化 /
- 近場(chǎng)通信 /
- 克拉美羅界
Abstract: Objective The growing demand for advanced service applications and the stringent performance requirements envisioned in future 6G networks have driven the development of Integrated Sensing and Communication (ISAC). By combining sensing and communication capabilities, ISAC enhances spectral efficiency and has attracted significant research attention. However, real-world signal propagation environments are often suboptimal, making it difficult to achieve optimal transmission and sensing performance under harsh or dynamic conditions. To address this, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) enable a full-space programmable wireless environment, offering an effective solution to enhance wireless system capabilities. In large-scale 6G industrial scenarios, STAR-RIS panels could be deployed on rooftops and walls for comprehensive coverage. As the number of reflecting elements increases, near-field effects become significant, rendering the conventional far-field assumption invalid. This paper explores the application of large-scale STAR-RIS in near-field ISAC systems, highlighting the role of near-field effects in enhancing sensing and communication performance. It highlights the importance of incorporating near-field phenomena into system design to exploit the additional degrees of freedom provided by large-scale STAR-RIS for improved localization accuracy and communication quality. Methods First, near-field ISAC system is formulated, where a large-scale STAR-RIS assists both sensing and communication processes. The theoretical framework of near-field steering vectors is applied to derive the steering vectors for each link, including those from the Base Station (BS) to the STAR-RIS, from the STAR-RIS to communication users, from the STAR-RIS to sensing targets, and from sensing targets to sensors. Based on these vectors, a system model is constructed to characterize the relationships among transmitted signals, signals reflected or transmitted via the STAR-RIS, and received signals for both communication and sensing.Next, the Cramér-Rao Bound (CRB) is then derived by calculating the Fisher Information Matrix (FIM) for three-dimensional (3D) parameter estimation of the sensing target, specifically its azimuth angle, elevation angle, and distance. The CRB serves as a theoretical benchmark for estimation accuracy. To optimize sensing performance, the CRB is minimized subject to communication requirements defined by a Signal-to-Interference-plus-Noise Ratio (SINR) constraint. The optimization involves jointly designing the BS precoding matrices, the transmit signal covariance matrices, and the STAR-RIS transmission and reflection coefficients to balance accurate sensing with reliable communication. Since the joint design problem is inherently nonconvex, an augmented Lagrangian formulation is employed. The original problem is decomposed into two subproblems using alternating optimization. Schur complement decomposition is first applied to transform the target function, and semidefinite relaxation is then used to convert each nonconvex subproblem into a convex one. These subproblems are alternately solved, and the resulting solutions are combined to achieve a globally optimized system configuration. This two-stage approach effectively reduces the computational complexity associated with high-dimensional, nonconvex optimization typical of large-scale STAR-RIS setups. Results and Discussions Simulation results under varying SINR thresholds indicate that the proposed STAR-RIS coefficient design achieves a lower CRB root than random coefficient settings (Fig. 2), demonstrating that optimizing the transmission and reflection coefficients of the STAR-RIS improves sensing precision. Additionally, the CRB root decreases as the number of Transmitting-Reflecting (T-R) elements increases in both the proposed and random designs, indicating that a larger number of T-R elements provides additional degrees of freedom. These degrees of freedom enable the system to generate more targeted beams for both sensing and communication, enhancing overall system performance.The influence of sensor elements on sensing accuracy is further analyzed by varying the number of sensing elements (Fig. 3). As the number of sensing elements increases, the CRB root declines, indicating that a larger sensing array improves the capture and processing of backscattered echoes, thereby enhancing the overall sensing capability. This finding highlights the importance of sufficient sensing resources to fully exploit the benefits of near-field ISAC systems.The study also examines three-dimensional localization of the sensing target under different SINR thresholds (Fig. 4, Fig. 5). Using Maximum Likelihood Estimation (MLE), the proposed method demonstrates highly accurate target positioning, validating the effectiveness of the joint design of precoding matrices, signal covariance, and STAR-RIS coefficients. Notably, near-field effects introduce distance as an additional dimension in the sensing process, absent in conventional far-field models. This additional dimension expands the parameter space, enhancing range estimation and contributing to more precise target localization. These results emphasize the potential of near-field ISAC for meeting the demanding localization requirements of future 6G systems.More broadly, the findings highlight the significant advantages of employing large-scale STAR-RIS in near-field settings for ISAC tasks. The improved localization accuracy demonstrates the synergy between near-field physics and advanced beam management techniques facilitated by STAR-RIS. These insights also suggest promising applications, such as industrial automation and precise positioning in smart factories, where reliable and accurate sensing is essential. Conclusions A large-scale STAR-RIS-assisted near-field ISAC system is proposed and investigated in this study. The near-field steering vectors for the links among the BS, STAR-RIS, communication users, sensing targets, and sensors are derived to construct an accurate near-field system model. The CRB for the 3D estimation of target location parameters is formulated and minimized by jointly designing the BS transmit beamforming matrices, the transmit signal covariance, and the STAR-RIS transmission and reflection coefficients, while ensuring the required communication quality. The nonconvex optimization problem is divided into two subproblems and addressed iteratively using semidefinite relaxation and alternating optimization techniques. Simulation results confirm that the proposed optimization scheme effectively reduces the CRB, enhancing sensing accuracy and demonstrating that near-field propagation provides an additional distance domain beneficial for both sensing and communication tasks. These findings suggest that near-field ISAC, enhanced by large-scale STAR-RIS, is a promising research direction for future 6G networks, combining increased degrees of freedom with high-performance integrated services.
- Reconfigurable Intelligent Surfaces (RIS) /
- Integrated Sensing and Communication (ISAC) /
- Near-field communications /
- Cramér-Rao Bound (CRB)

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圖 1 導(dǎo)向矢量的推導(dǎo)模型

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圖 2 CRB根值與通信用戶(hù)SINR閾值的關(guān)系

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圖 3 CRB根值與傳感元件個(gè)數(shù)${N_{\mathrm{s}}}$的關(guān)系

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圖 4 感知目標(biāo)定位的3維切片圖，$\tilde \gamma = 0$

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圖 5 感知目標(biāo)定位的3維切片圖，$\tilde \gamma {\text{ = 20}}\;{\text{dB}}$

下載: 全尺寸圖片幻燈片

1 優(yōu)化過(guò)程算法

初始化參數(shù)：${\nu ^l}$, ${\eta ^l}$, ${{\boldsymbol{\varLambda}} ^l}$；設(shè)置外層收斂精度$0 < \chi < 1$，迭代　次數(shù)初始化$m = 1$，最大迭代次數(shù)為${M_{{\text{max}}}}$；內(nèi)層收斂精度　$0 < c < 1$，迭代次數(shù)為$u$，最大迭代次數(shù)為${U_{{\text{max}}}}$，懲罰因子更　新參數(shù)為$0 < d < 1$；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 $\left\| {{\text{tr}}{{({{\boldsymbol{E}}^{ - 1}})}^u} - {\text{tr}}{{({{\boldsymbol{E}}^{ - 1}})}^{u - 1}}} \right\| \ge c$ or $u \le {U_{{\text{max}}}}$ do
(4) 　　固定${\varepsilon ^u}$，求得$ \mathcal{S}\mathcal{P}1 $的最優(yōu)解$\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 $的最優(yōu)解$\tilde \varepsilon $并更新${\left( {\tilde \varepsilon } \right)^u}$；
(6) 　　設(shè)置迭代次數(shù)$u = u + 1$；
(7) 　End while
(8) 　If $h\left( {{\nu ^m}} \right) \le 0.95h\left( {{\nu ^{m - 1}}} \right)$ then
(9) 　　將${\left( {\tilde \varepsilon } \right)^u}$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) 設(shè)置迭代次數(shù)$m = m + 1$,更新$h\left( {{\nu ^m}} \right) = {\left\\| {{{\bar {\boldsymbol{O}}}^m}} \right\\|_\infty }$；
(13) End while

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