STAR-RIS辅助URLLC-NOMA系统的联合波束成形设计

朱建月; 吴雨桐; 陈晓; 谢亚琴; 许尧; 张治中

doi:10.11999/JEIT240717

STAR-RIS辅助URLLC-NOMA系统的联合波束成形设计

doi: 10.11999/JEIT240717

1.
南京信息工程大学电子与信息工程学院南京 210044
2.
东南大学网络空间安全学院南京 211189
3.
南京信息工程大学人工智能学院(未来技术学院) 南京 210044

基金项目: 国家自然科学基金(62101273)，江苏省自然科学基金(BK20220439, BK20220438)，江苏省高等学校基础科学(自然科学)研究面上项目(22KJB510005, 22KJB510033)，江苏省重点研发计划(BE2023088)，江苏省双创团队((2023)200008号)

详细信息

作者简介:
朱建月：女，讲师，研究方向为多址接入技术，大规模MIMO无线传输技术，无线资源管理等

吴雨桐：女，学士，研究方向为多天线传输技术，多址接入技术等

陈晓：女，讲师，研究方向为智能超表面，大规模MIMO系统，基于深度学习通信技术等

谢亚琴：女，副教授，研究方向为无线定位技术，卫星导航，通信网络规划与优化等

许尧：男，讲师，博士，研究方向为非正交多址接入，正交时频空间调制等

张治中：男，教授，博士，研究方向为移动通信，通信网络与测试技术等

通讯作者:
朱建月　zhujy@nuist.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2024-08-19
- 修回日期: 2025-02-14
- 网络出版日期: 2025-02-24

Joint Beamforming Design for STAR-RIS Assisted URLLC-NOMA System

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing210044, China
2.
School of Cyberspace Security, Southeast University, Nanjing211189, China
3.
School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science, Nanjing University of Information Science and Technology, Nanjing210044, China

Funds: The National Natural Science Foundation of China (62101273), The Natural Science Foundation of Jiangsu Province of China (BK20220439, BK20220438), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 22KJB510005, 22KJB510033), Jiangsu Provincial Key Research and Development Program (BE2023088), Jiangsu Provincial Innovation and Entrepreneurship Team (No. (2023)200008)

摘要

摘要: 针对超可靠低时延通信(URLLC)场景，该文研究了融合透射与反射功能的智能超表面(STAR-RIS)辅助的非正交多址接入(NOMA)系统的传输设计。具体而言，该文联合设计了基站端的波束成形向量、RIS端的透射相移矩阵和反射相移矩阵，以在满足基站总功率约束的条件下实现能耗最小化。为解决所提出的非凸问题，该文首先分析了有限块长传输下的用户速率函数特性，并据此将优化问题进行等价转换。随后，采用交替优化和半正定松弛(SDR)方法来解决联合波束设计问题。实验结果表明，与正交多址接入和传统RIS方案相比，所提出的方法在能耗性能上有显著提升。
- 智能反射面 /
- 非正交多址接入技术 /
- 波束成形设计
Abstract: Objective This paper addresses the energy efficiency challenge in Ultra-Reliable Low-Latency Communication (URLLC) systems, crucial for mission-critical applications such as industrial automation and remote surgery. The integration of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) with Non-Orthogonal Multiple Access (NOMA) is proposed to improve spectral efficiency and coverage while meeting URLLC’s stringent reliability and latency requirements. However, the joint optimization of base station beamforming, STAR-RIS transmission, and reflection matrices presents a non-trivial problem due to non-convexity and coupled variables. This work aims to minimize energy consumption under a total power constraint by jointly designing these parameters, advancing STAR-RIS-aided NOMA systems for URLLC. Methods To address the non-convex optimization problem, the proposed methodology involves several key steps. First, the user rate function under finite blocklength transmission is analyzed, considering the specific requirements of URLLC. This analysis facilitates the reformulation of the original problem into an equivalent form more amenable to optimization. Specifically, the rate function is approximated using a Taylor series expansion, and the effect of finite blocklength on decoding error probability is incorporated into the optimization framework.Next, an alternating optimization framework is adopted to decouple the joint design problem into subproblems, each focused on optimizing either the base station beamforming, the STAR-RIS transmission matrix, or the reflection matrix. Semidefinite Relaxation (SDR) techniques are then applied to address the non-convexity of these subproblems, ensuring efficient and tractable solutions. The SDR method transforms the original non-convex constraints into convex ones by relaxing certain matrix rank constraints, which are subsequently recovered using randomization techniques.The proposed approach is validated through extensive simulations, comparing its performance with Orthogonal Multiple Access (OMA) and traditional RIS-aided schemes. The simulation setup includes a multi-user scenario with varying channel conditions, blocklengths, and reliability requirements. Results and Discussions The main contributions of this paper are summarized as follows:(1) Joint Optimization of Active and Passive Beamforming Vectors: To minimize system transmission power, the paper jointly optimizes the active beamforming vector at the base station and the passive beamforming vector at the reflective surface, presenting an efficient joint beamforming design algorithm (Table 1). (2) Validation and Energy Efficiency Comparison: Experimental results confirm the effectiveness of the proposed joint beamforming design. A comparison of energy consumption performance for STAR-RIS under different modes is provided. Specifically, the proposed STAR-RIS-aided NOMA scheme demonstrates a significant reduction in power consumption compared to OMA and conventional RIS-aided systems (Fig. 2 and Fig. 5). The proposed joint beamforming and STAR-RIS optimization framework effectively addresses the trade-offs between energy consumption, reliability, and latency in URLLC systems. Conclusions This paper presents a comprehensive framework for the transmission design of STAR-RIS-aided NOMA systems in URLLC scenarios. By jointly optimizing beamforming, transmission, and reflection matrices, the proposed method significantly enhances energy efficiency while meeting the stringent requirements of URLLC. The use of alternating optimization and SDR techniques effectively addresses the non-convexity of the problem, providing practical and scalable solutions.The results highlight the potential of STAR-RIS-aided NOMA systems to support next-generation wireless communication applications, laying the foundation for further research in this area. Future work will explore the integration of machine learning techniques to further enhance the performance and adaptability of the proposed framework. Additionally, the impact of hardware impairments and imperfect channel state information on system performance will be investigated to ensure robustness in real-world deployments.
- Reconfigurable Intelligent Surface /
- Non-Orthogonal Multiple Access (NOMA) /
- Beamforming Design

HTML全文

图 1 STAR-RIS辅助NOMA URLLC系统模型

下载: 全尺寸图片幻灯片

图 2 在用户不同的目标数据速率下天线数量与传输功率的关系

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图 3 在不同块长$l$ 下天线数量与发射功率的关系

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图 4 在不同解码错误概率$ \epsilon $下天线数量与发射功率的关系

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图 5 ${\beta _r}$和${\beta _t}$不同取值时天线数量与发射功率的关系

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1 主被动波束成形联合优化设计算法

1. 初始化：迭代次数iter=0，${\boldsymbol{\Theta }}_t^{(0)}$，${\boldsymbol{\Theta }}_r^{(0)}$，临界值$\eta \gt 0$；
2. 重复步骤3–步骤4：
3. 给定${\boldsymbol{\Theta }}_t^{({\text{iter}})}$，${\boldsymbol{\Theta }}_r^{({\text{iter}})}$，通过求解问题式得到${\boldsymbol{w}}_i^$，$i = 1,2$，　令${\boldsymbol{w}}_i^{({\text{iter}})} = {\boldsymbol{w}}_i^$，$i = 1,2$；
4. 给定${\boldsymbol{w}}_i^{({\text{iter}})}$，$i = 1,2$，通过求解问题式得到${\boldsymbol{\Theta }}_t^$和${\boldsymbol{\Theta }}_r^$，令　${\boldsymbol{\Theta }}_t^{({\text{iter}})} = {\boldsymbol{\Theta }}_t^$，${\boldsymbol{\Theta }}_r^{({\text{iter}})} = {\boldsymbol{\Theta }}_r^$；
5. 直到系统的最小功率收敛到精度$\eta $或达到指定的迭代次数。

下载: 导出CSV

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