智能超表面辅助多用户系统的通用低复杂度波束成形设计

陈晓; 施建锋; 朱建月; 潘存华

doi:10.11999/JEIT240051

智能超表面辅助多用户系统的通用低复杂度波束成形设计

doi: 10.11999/JEIT240051

1.
南京信息工程大学人工智能学院(未来技术学院) 南京 210044
2.
南京信息工程大学电子与信息工程学院南京 210044
3.
东南大学移动通信国家重点实验室南京 210096

基金项目: 国家自然科学基金(62101273, 62201274)，江苏省自然科学基金(BK20210641, BK20220439)

详细信息

作者简介:
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

施建锋：男，副教授，研究方向为天地一体化网络、波束成形理论、基于机器学习通信技术等

朱建月：女，讲师，研究方向为非正交多址接入技术、无线资源管理等

潘存华：男，教授，研究方向为无线通信传输设计，信道估计与定位，通感一体化，AI辅助通信等

通讯作者:
陈晓　x.chen@nuist.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 60
- HTML全文浏览量: 20
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-24
- 修回日期: 2024-03-15
- 网络出版日期: 2024-03-26

General Low-complexity Beamforming Designs for Reconfigurable Intelligent Surface-aided Multi-user Systems

1.
School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62101273, 62201274), The Natural Science Foundation of Jiangsu Province of China (BK20210641, BK20220439)

摘要

摘要: 针对可重构智能超表面(RIS)辅助多用户系统中基站和RIS联合波束成形设计问题，该文提出通用低复杂度联合波束成形设计方案。首先，分析RIS辅助多用户系统以最大化和数据速率为目标的联合波束成形非凸优化问题。其次，利用波束导向矢量近似正交性设计RIS反射矩阵，进一步利用迫零方法设计基站发射波束成形，并对多用户进行功率分配优化。最后，讨论该方案适用性并对比该方案的计算复杂度相比现有方案降低了一个数量级。仿真结果表明，所提通用低复杂度波束成形设计可以获得较高和数据速率，并且采用最优功率分配可以进一步提高和数据速率。此外，仿真结果和理论分析都表明系统和数据速率随RIS位置的变化而变化，该结论为RIS位置的选择提供参考依据。
- 智能超表面 /
- 波束成形 /
- 和数据速率 /
- 低复杂度
Abstract: General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
- Reconfigurable Intelligent Surface (RIS) /
- Beamforming /
- Sum data rate /
- Low complexity

HTML全文

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 和数据速率与反射元件数$ N $的关系，$ K = 4 $, $ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

下载: 全尺寸图片幻灯片

图 3 LoS信道和莱斯信道和数据速率对比，$ K = 4 $,$ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

下载: 全尺寸图片幻灯片

图 4 和数据速率与RIS和UEs间距$ {d_{{\text{RU}}}} $的关系，$ K = 4 $, $ N = 64 $

下载: 全尺寸图片幻灯片

图 5 和数据速率与用户数$ K $的关系，$ N = 64 $, $ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

下载: 全尺寸图片幻灯片

1 低复杂度联合波束成形设计算法

输入：初始化$ \left( {{{\boldsymbol{W}}}{\text{,}}{ {\boldsymbol{\varTheta }}}{\text{,}}{ {\boldsymbol{P}}}} \right) $
步骤1　基于已知BS-RIS信道$ {{\boldsymbol{G}}} $和RIS-UEs信道$ {{{\boldsymbol{H}}}_{\text{r}}} $和引理1，根　据式(14)计算RIS反射矩阵$ {{\boldsymbol{\varTheta}} } $；
步骤2　基于ZF理论，根据式(19)计算BS发射波束成形$ {{\boldsymbol{W}}} $；
步骤3　基于WF理论，根据式(23)计算功率分配矢量$ {{\boldsymbol{P}}} $；
步骤4　输出优化得到的$ \left( {{{\boldsymbol{W}}}{\text{,}}{ {\boldsymbol{\varTheta}} }{\text{,}}{ {\boldsymbol{P}}}} \right) $。

下载: 导出CSV

表 1 波束成形方案计算复杂度对比

文献	复杂度	参数
文献 [3]	$ \mathcal{O}\left( {{N^6}} \right) $	N：RIS反射元件数
文献[18]	$ \mathcal{O}\left( {{I_{\text{o}}}\left( {{I_{\text{a}}}{M^2}{K^2} + {I_{\text{p}}}{N^2}} \right)} \right) $	M：基站发射天线数
文献[16]	$ \mathcal{O}\left( {Q\left( {{M^3} + M{N^2} + N!} \right)} \right) $	K：用户数
文献[17]	$ \mathcal{O}\left( {NI\left( {K{M^2}} \right)} \right) $	$ {I_{\text{o}}} $,$ {I_{\text{a}}} $,$ {I_{\text{p}}} $,$ I $：迭代次数
本文	$ \mathcal{O}\left( {N + {K^2}M + {K^3}} \right) $	Q：预设训练集数目

下载: 导出CSV

参考文献(22)

[1]	YOU Xiaohu, WANG Chengxiang, HUANG Jie, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts[J]. Science China Information Sciences, 2021, 64(1): 110301. doi: 10.1007/s11432-020-2955-6.
[2]	ZHANG Zhengquan, XIAO Yue, MA Zheng, et al. 6G wireless networks: Vision, requirements, architecture, and key technologies[J]. IEEE Vehicular Technology Magazine, 2019, 14(3): 28–41. doi: 10.1109/MVT.2019.2921208.
[3]	WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025.
[4]	HUANG Chongwen, ZAPPONE A, ALEXANDROPOULOS G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Transactions on Wireless Communications, 2019, 18(8): 4157–4170. doi: 10.1109/TWC.2019.2922609.
[5]	JIANG Hao, RUAN Chengyao, ZHANG Zaichen, et al. A general wideband non-stationary stochastic channel model for intelligent reflecting surface-assisted MIMO communications[J]. IEEE Transactions on Wireless Communications, 2021, 20(8): 5314–5328. doi: 10.1109/TWC.2021.3066806.
[6]	WU Qingqing, ZHANG Shuowen, ZHENG Beixiong, et al. Intelligent reflecting surface-aided wireless communications: A tutorial[J]. IEEE Transactions on Communications, 2021, 69(5): 3313–3351. doi: 10.1109/TCOMM.2021.3051897.
[7]	李兴旺, 田志发, 张建华, 等. IRS辅助NOMA网络下隐蔽性能研究[J]. 中国科学: 信息科学, 2023. doi: 10.1360/SSI-2023-0174. LI Xingwang, TIAN Zhifa, ZHANG Jianhua, et al. Performance analysis of covert communication in IRS-assisted NOMA networks[J]. Scientia Sinica Informationis, 2023. doi: 10.1360/SSI-2023-0174.
[8]	LIU Yuanwei, LIU Xiao, MU Xidong, et al. Reconfigurable intelligent surfaces: Principles and opportunities[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1546–1577. doi: 10.1109/COMST.2021.3077737.
[9]	YAN Wenjing, YUAN Xiaojun, HE Zhenqing, et al. Passive beamforming and information transfer design for reconfigurable intelligent surfaces aided multiuser MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1793–1808. doi: 10.1109/JSAC.2020.3000811.
[10]	GUO Huayan, LIANG Yingchang, CHEN Jie, et al. Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3064–3076. doi: 10.1109/TWC.2020.2970061.
[11]	PAN Cunhua, REN Hong, WANG Kezhi, et al. Multicell MIMO communications relying on intelligent reflecting surfaces[J]. IEEE Transactions on Wireless Communications, 2020, 19(8): 5218–5233. doi: 10.1109/TWC.2020.2990766.
[12]	LIU Sifan, LIU Rang, LI Ming, et al. Joint BS-RIS-user association and beamforming design for RIS-assisted cellular networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(5): 6113–6128. doi: 10.1109/TVT.2022.3231347.
[13]	李国权, 党刚, 林金朝, 等. RIS辅助的MISO系统安全鲁棒波束赋形算法[J]. 电子与信息学报, 2023, 45(8): 2867–2875. doi: 10.11999/JEIT220894. LI Guoquan, DANG Gang, LIN Jinzhao, et al. Secure and robust beamforming algorithm for RIS assisted MISO systems[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2867–2875. doi: 10.11999/JEIT220894.
[14]	WANG Peilan, FANG Jun, DAI Linglong, et al. Joint transceiver and large intelligent surface design for massive MIMO mmWave systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(2): 1052–1064. doi: 10.1109/TWC.2020.3030570.
[15]	HE Zhenyao, SHEN Hong, XU Wei, et al. Low-cost passive beamforming for RIS-aided wideband OFDM systems[J]. IEEE Wireless Communications Letters, 2022, 11(2): 318–322. doi: 10.1109/LWC.2021.3126852.
[16]	AN Jiancheng, XU Chao, GAN Lu, et al. Low-complexity channel estimation and passive beamforming for RIS-assisted MIMO systems relying on discrete phase shifts[J]. IEEE Transactions on Communications, 2022, 70(2): 1245–1260. doi: 10.1109/TCOMM.2021.3127924.
[17]	ALMEKHLAFI M, ARFAOUI M A, ASSI C, et al. A low complexity passive beamforming design for reconfigurable intelligent surface (RIS) in 6G networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(5): 6309–6321. doi: 10.1109/TVT.2022.3233469.
[18]	SU Ruochen, DAI Linglong, and NG D W K. Wideband precoding for RIS-aided THz communications[J]. IEEE Transactions on Communications, 2023, 71(6): 3592–3604. doi: 10.1109/TCOMM.2023.3263230.
[19]	ZHANG Zijian and DAI Linglong. A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network[J]. IEEE Transactions on Signal Processing, 2021, 69: 4085–4101. doi: 10.1109/TSP.2021.3088755.
[20]	WU Qingqing and ZHANG Rui. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1): 106–112. doi: 10.1109/MCOM.001.1900107.
[21]	LI Xingwang, GAO Xuesong, LIU Yingting, et al. Overlay CR-NOMA assisted intelligent transportation system networks with imperfect SIC and CEEs[J]. Chinese Journal of Electronics, 2023, 32(6): 1258–1270. doi: 10.23919/cje.2022.00.071.
[22]	TANG Wankai, CHEN Mingzheng, CHEN Xiangyu, et al. Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 421–439. doi: 10.1109/TWC.2020.3024887.