智能超表面辅助的非正交多址接入车联网通感一体传输与资源分配优化

李美玲; 朱芸灿; 申陈宁; 李兴旺

doi:10.11999/JEIT240842

智能超表面辅助的非正交多址接入车联网通感一体传输与资源分配优化

doi: 10.11999/JEIT240842

1.
太原科技大学电子信息工程学院太原 030024
2.
山西通信通达微波技术有限公司太原 030006
3.
河南理工大学物理与电子信息学院焦作 454000

基金项目: 国家重点研发计划项目(2024YFE0200300)，山西省科技创新人才团队专项计划(202304051001035)，太原市双百攻关揭榜挂帅项目(2024TYJB0134)，山西省科技成果转化引导专项(202204021301055)，山西省留学人员科技活动择优资助项目(20240023)，山西省科研实践创新类项目(2023KY647)

详细信息

作者简介:
李美玲：女，教授，研究方向为通信感知一体化，移动通信，包括认知无线电、V2X、非正交多址和物理层安全等

朱芸灿：女，硕士生，研究方向为通信感知一体化、V2X、非正交多址技术等

申陈宁：男，研究方向为移动通信、电力工业、电信技术等

李兴旺：男，副教授，研究方向为无线通信、通信感知一体化、RIS、反向散射通信等

通讯作者:
李美玲　meilingli@tyust.edu.cn

中图分类号: TN929.5
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- 文章访问数: 63
- HTML全文浏览量: 19
- PDF下载量: 19
- 被引次数: 51
出版历程
- 收稿日期: 2024-10-08
- 修回日期: 2025-02-28
- 网络出版日期: 2025-03-25

RIS-Assisted ISAC with Non-orthogonal Multiple Access Transmission and Resource Allocation Optimization in Vehicular Networks

1.
①(School of Electronics Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
2.
Shanxi Communication Tongda Microwave Technology Co., Ltd, Taiyuan 030006, China
3.
School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

Funds: The National Key Research and Development Program of China (2024YFE0200300), Special Fund for Science and Technology Innovation Teams of Shanxi Province (202304051001035), Taiyuan double hundred Research Project (2024TYJB0134), Shanxi Province Science and Technology Achievement Transformation Guidance Special Project (202204021301055), The Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (20240023), Shanxi Scientific Research Practice Innovation Project (2023KY647)

摘要

摘要: 为应对6G密集城市环境下车联网(V2X)通信和传感路径受限问题，该文提出了一种基于可重构智能超表面(RIS)辅助的通感一体化(ISAC) V2X系统框架。针对非视距(NLOS)下的车辆移动性，采用扩展卡尔曼滤波(EKF)算法结合，结合ISAC回波信号中的实时信道状态信息(CSI)，实现对移动车辆位置的跟踪与预测。该文提出基于非正交多址接入技术(NOMA)的多车辆间功率分配优化方案，在保证感知精度的同时提升下行链路通信总速率，并引入Karush-Kuhn-Tucker(KKT)条件作为反馈机制，避免陷入局部最优。仿真结果表明，所提系统在通信性能和感知性能方面优于传统的RIS辅助ISAC-V2X系统。
- 车联网 /
- 通信感知一体化 /
- 可重构智能超表面 /
- 扩展卡尔曼滤波 /
- 非正交多址接入技术
Abstract: Objective To address the issue of limited V2X communication and sensing paths in 6G dense urban environments, a RIS-assisted ISAC-V2X system framework is proposed. Considering vehicle mobility under Non-Line-of-Sight (NLOS) conditions, the Extended Kalman Filter (EKF) algorithm is utilized to track and predict the positions of moving vehicles by combining real-time Channel State Information (CSI) from the ISAC echo signals. A multi-vehicle power allocation optimization scheme based on Non-Orthogonal Multiple Access (NOMA) is introduced to enhance the downlink communication sum rate while maintaining sensing accuracy. The Karush-Kuhn-Tucker (KKT) conditions are incorporated as a feedback mechanism to prevent the system from converging to a local optimum. Simulation results demonstrate that the proposed system outperforms the traditional RIS-assisted ISAC-V2X system in terms of both communication and sensing performance. Methods This study establishes a RIS-assisted ISAC-V2X-NOMA system model. Considering vehicle mobility in NLOS conditions, the EKF algorithm is employed to track and predict vehicle locations base on real-time CSI from the ISAC signals. Subsequently, a multi-vehicle power allocation optimization scheme based on NOMA is proposed, with the KKT conditions introduced to avoid local optima and ensure global optimality. To comprehensively evaluate channel estimation performance, 1000 Monte Carlo simulations are conducted, and performance analyses are carried out on MATLAB with comparisons to traditional RIS-assisted ISAC-V2X systems under different scenarios, ultimately validating the superiority of the proposed system. Results and Discussions The sensor tracking performance of the proposed system is presented, which indicate that the introduction of RIS significantly improves the angle and distance tracking accuracy. As the number of RIS reflection elements increases, the system's Root Mean Square Error (RMSE) decreases, validating the effectiveness of RIS in complex dynamic environments. Secondly, the communication performance analysis between the proposed system and the traditional system under different antenna configurations is presented, where one can observe that the communication sum rate increases as the vehicle approaches the RIS surface and decreases as it moves away, which can be also improved by increasing the number of antennas. In dense environments with limited resources, the proposed system obviously outperforms the traditional system in terms of communication sum rate under the same RIS configuration. Finally, one can also observe that power allocation optimization using NOMA allows more efficient resource management and reduced inter-user interference, further improving communication rates. These results demonstrate the significant advantages of the proposed system in terms of both communication and sensing performance in V2X systems. Conclusions This paper proposes an RIS-assisted ISAC-V2X-NOMA system framework. By utilizing RIS to dynamically adjust the propagation path of ISAC signals and designing an EKF-based vehicle tracking and prediction method, efficient real-time vehicle sensing and communication are achieved. Furthermore, a multi-vehicle power allocation optimization scheme based on NOMA is proposed to enhance communication rate and resource utilization. The results suggest that the proposed system not only reduces pilot signal overhead but also enhances the overall system performance.
- V2X /
- Integrated Sensing and Communication (ISAC) /
- Reconfigurable Intelligent Surface (RIS) /
- Extended Kalman Filter (EKF) /
- Non-Orthogonal Multiple Access (NOMA)

HTML全文

图 1 系统模型图

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图 2 3维车辆状态演化示意图

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图 3 ISAC-V2X-NOMA系统模拟示意图

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图 4 VU₁和VU₂传感性能分析

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图 5 不同天线数下不同方案的通信性能分析

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图 6 不同RIS反射单元数量下车辆通信性能分析

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图 7 不同方案下的通信性能分析

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流程1 车辆功率分配算法流程
输入：用户数 $K$ 、信噪比 ${{\mathrm{SINR}}_{k,n}}$ 、总发射功率 ${P_{\mathrm{T}}}$ 、噪声功率　 $\sigma _{\mathrm{c}}^2$ 、角度约束 ${\varepsilon _\theta }$ 、距离约束 ${\varepsilon _d}$ 。
输出：每个用户的最优功率分配 ${\rho _{k,n}}$ 。
步骤1　定义目标函数 $\max \;f\left( \rho \right)$ 。
步骤2　构造拉格朗日函数 $L\left( {\rho ,\xi ,\eta ,{\nu _\theta },{\nu _d}} \right)$ 。
步骤3　导出KKT条件方程组。
步骤4　使用CVX工具箱求解目标函数 $\min \; - f\left( \rho \right)$ 。
步骤5　验证解决方案的KKT条件，　　若满足，输出最优功率分配，　　否则，重复步骤2–4。
步骤6　输出用户的最优功率分配 ${\rho _{k,n}}$ 。

下载: 导出CSV

表 1 车辆初始参数表

	VU₁	VU₂
距离(m)	25	20
角度(°)	9.2	7.66
反射系数	0.5+0.5j	1+j
速度(m/s)	20	20

下载: 导出CSV

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