STAR-RIS輔助URLLC-NOMA系統(tǒng)的聯(lián)合波束成形設(shè)計(jì)

朱建月; 吳雨桐; 陳曉; 謝亞琴; 許堯; 張治中

doi:10.11999/JEIT240717

STAR-RIS輔助URLLC-NOMA系統(tǒng)的聯(lián)合波束成形設(shè)計(jì)

doi: 10.11999/JEIT240717 cstr: 32379.14.JEIT240717

1.
南京信息工程大學(xué)電子與信息工程學(xué)院南京 210044
2.
東南大學(xué)網(wǎng)絡(luò)空間安全學(xué)院南京 211189
3.
南京信息工程大學(xué)人工智能學(xué)院(未來技術(shù)學(xué)院) 南京 210044

基金項(xiàng)目: 國家自然科學(xué)基金(62101273)，江蘇省自然科學(xué)基金(BK20220439, BK20220438)，江蘇省高等學(xué)?；A(chǔ)科學(xué)(自然科學(xué))研究面上項(xiàng)目(22KJB510005, 22KJB510033)，江蘇省重點(diǎn)研發(fā)計(jì)劃(BE2023088)，江蘇省雙創(chuàng)團(tuán)隊(duì)((2023)200008號)

詳細(xì)信息

作者簡介:
朱建月：女，講師，研究方向?yàn)槎嘀方尤爰夹g(shù)、大規(guī)模MIMO無線傳輸技術(shù)、無線資源管理等

吳雨桐：女，學(xué)士，研究方向?yàn)槎嗵炀€傳輸技術(shù)、多址接入技術(shù)等

陳曉：女，講師，研究方向?yàn)橹悄艹砻?、大?guī)模MIMO系統(tǒng)、基于深度學(xué)習(xí)通信技術(shù)等

謝亞琴：女，副教授，研究方向?yàn)闊o線定位技術(shù)、衛(wèi)星導(dǎo)航、通信網(wǎng)絡(luò)規(guī)劃與優(yōu)化等

許堯：男，講師，博士，研究方向?yàn)榉钦欢嘀方尤?、正交時(shí)頻空間調(diào)制等

張治中：男，教授，博士，研究方向?yàn)橐苿?dòng)通信、通信網(wǎng)絡(luò)與測試技術(shù)等

通訊作者:
朱建月　zhujy@nuist.edu.cn

中圖分類號: TN929.5
計(jì)量
- 文章訪問數(shù): 260
- HTML全文瀏覽量: 115
- PDF下載量: 49
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-08-19
- 修回日期: 2025-02-14
- 網(wǎng)絡(luò)出版日期: 2025-02-24
- 刊出日期: 2025-02-28

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

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Cyberspace Security, Southeast University, Nanjing 211189, China
3.
School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science, Nanjing University of Information Science and Technology, Nanjing 210044, 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 (22KJB510005, 22KJB510033), Jiangsu Provincial Key Research and Development Program (BE2023088), Jiangsu Provincial Innovation and Entrepreneurship Team ((2023)200008)

摘要

摘要: 針對超可靠低時(shí)延通信(URLLC)場景，該文研究了融合透射與反射功能的智能超表面(STAR-RIS)輔助的非正交多址接入(NOMA)系統(tǒng)的傳輸設(shè)計(jì)。具體而言，該文聯(lián)合設(shè)計(jì)了基站端的波束成形向量、RIS端的透射相移矩陣和反射相移矩陣，以在滿足基站總功率約束的條件下實(shí)現(xiàn)能耗最小化。為解決所提出的非凸問題，該文首先分析了有限塊長傳輸下的用戶速率函數(shù)特性，并據(jù)此將優(yōu)化問題進(jìn)行等價(jià)轉(zhuǎn)換。隨后，采用交替優(yōu)化和半正定松弛(SDR)方法來解決聯(lián)合波束設(shè)計(jì)問題。實(shí)驗(yàn)結(jié)果表明，與正交多址接入和傳統(tǒng)RIS方案相比，所提出的方法在能耗性能上有顯著提升。
- 智能反射面 /
- 非正交多址接入技術(shù) /
- 波束成形設(shè)計(jì)
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 (Algorithm 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

圖 1 STAR-RIS輔助NOMA URLLC系統(tǒng)模型

下載: 全尺寸圖片幻燈片

圖 2 在用戶不同的目標(biāo)數(shù)據(jù)速率下天線數(shù)量與傳輸功率的關(guān)系

下載: 全尺寸圖片幻燈片

圖 3 在不同塊長$l$ 下天線數(shù)量與發(fā)射功率的關(guān)系

下載: 全尺寸圖片幻燈片

圖 4 在不同解碼錯(cuò)誤概率$ \varepsilon $下天線數(shù)量與發(fā)射功率的關(guān)系

下載: 全尺寸圖片幻燈片

圖 5 ${\beta _{\mathrm{r}}}$和${\beta _{\mathrm{t}}}$不同取值時(shí)天線數(shù)量與發(fā)射功率的關(guān)系

下載: 全尺寸圖片幻燈片

1 主被動(dòng)波束成形聯(lián)合優(yōu)化設(shè)計(jì)算法

1. 初始化：迭代次數(shù)iter=0，${\boldsymbol{\varTheta }}_{\mathrm{t}}^{(0)}$, ${\boldsymbol{\varTheta }}_{\mathrm{r}}^{(0)}$，臨界值$\eta \gt 0$；
2. 重復(fù)步驟3–步驟4：
3. 給定${\boldsymbol{\varTheta }}_{\mathrm{t}}^{({\text{iter}})}$, ${\boldsymbol{\varTheta }}_{\mathrm{r}}^{({\text{iter}})}$，通過求解問題式得到${\boldsymbol{w}}_i^$，$i = 1,2$，　令${\boldsymbol{w}}_i^{({\text{iter}})} = {\boldsymbol{w}}_i^$，$i = 1,2$；
4. 給定${\boldsymbol{w}}_i^{({\text{iter}})}$，$i = 1,2$，通過求解問題式得到${\boldsymbol{\varTheta }}_{\mathrm{t}}^$和${\boldsymbol{\varTheta }}_{\mathrm{r}}^$，令　${\boldsymbol{\varTheta }}_{\mathrm{t}}^{({\text{iter}})} = {\boldsymbol{\varTheta }}_{\mathrm{t}}^$，${\boldsymbol{\varTheta }}_{\mathrm{r}}^{({\text{iter}})} = {\boldsymbol{\varTheta }}_{\mathrm{r}}^$；
5. 直到系統(tǒng)的最小功率收斂到精度$\eta $或達(dá)到指定的迭代次數(shù)。

下載: 導(dǎo)出CSV

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