RIS辅助MIMO NOMA系统中利用统计CSI的下行传输方法

陆佳程; 王斌; 张军; 倪艺洋

doi:10.11999/JEIT230630

RIS辅助MIMO NOMA系统中利用统计CSI的下行传输方法

doi: 10.11999/JEIT230630

陆佳程¹,
王斌¹,
张军^1, ,,
倪艺洋²

1.
南京邮电大学通信与信息工程学院南京 210003
2.
江苏第二师范学院物理与信息工程学院南京 210013

基金项目: 国家自然科学基金(62071247)，江苏省高校自然科学基金(BK2021022532)

详细信息

作者简介:
陆佳程：男，博士生，研究方向为超大规模MIMO、毫米波通信、NOMA

王斌：男，硕士，研究方向为大规模MIMO, NOMA

张军：男，博士，教授，研究方向为超大规模MIMO、无人机通信、人工智能通信、毫米波通信、RIS辅助通信以及物理层安全等

倪艺洋：女，博士，教授，研究方向为智能无线通信、智能超表面等

通讯作者:
张军　zhangjun@njupt.edu.cn

中图分类号: TN92
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- 被引次数: 7
出版历程
- 收稿日期: 2023-06-25
- 修回日期: 2023-12-16
- 网络出版日期: 2023-12-25
- 刊出日期: 2024-04-24

Downlink Transmission for RIS-Assisted MIMO NOMA by Exploiting Statistical CSI

1.
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China

Funds: The National Natural Science Foundation of China (62071247), The Natural Science Foundation of Jiangsu Province (BK2021022532)

摘要

摘要: 针对可重构智能反射表面(RIS)辅助多输入多输出(MIMO)非正交多址接入(NOMA)下行传输系统，该文提出利用统计信道状态信息(CSI)的基站发送协方差矩阵与RIS相移矩阵设计方法。首先，在莱斯空间相关信道假设下，利用大维随机矩阵理论，推导了RIS辅助MIMO NOMA系统遍历和速率的确定性表达式；然后，在弱用户速率约束与发送功率受限的条件下，通过最大化确定性大系统近似和速率，利用统计CSI，分别设计了强、弱用户的次优发送协方差矩阵和RIS的相移矩阵。仿真结果表明，所推导的近似表达式具有很好的近似效果，所设计的发送协方差矩阵和相移矩阵能显著提升系统的和速率。
- 统计信道状态信息 /
- 非正交多址接入 /
- 可重构智能反射表面 /
- 发送协方差 /
- 大维随机矩阵理论
Abstract: For a Reconfigurable Intelligent Surface (RIS)-assisted Multiple Input Multiple Output (MIMO) Non-Orthogonal Multiple Access (NOMA) downlink system, the transmit covariances matrix at the base station and the phase-shifting matrix at the RIS are jointly designed based on statistical Channel State Information (CSI). First, in the spatial correlated Rician channel, a deterministic large-system approximation for the ergodic sum rate is obtained for an RIS-assisted MIMO-NOMA system by resorting to the large-dimensional random matrix theory. Then, by maximizing the approximated sum rate, the transmit covariances matrix for the strong and weak user as well as the phase-shifting matrix at the RIS are designed based on statistical CSI under the constraints of total transmit power and the rates threshold for weak user. The simulations validate the high accuracy of our approximations and our proposed transmit covariances and phase-shifting matrix can improve the system performances significantly.

HTML全文

图 1 基于RIS辅助的MIMO NOMA下行无线通信系统

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图 2 遍历和速率及其确定性等价表达式与BS发送功率的关系

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图 5 遍历和速率和BS与RIS间距离的关系

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图 3 交替优化算法的收敛性

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图 4 遍历和速率与RIS反射单元数量的关系

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图 6 空间相关性对系统性能影响

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1 交替优化算法

(1) 初始化： ${\boldsymbol{Q}}_1^{(0)} = {{\boldsymbol{I}}_N},{\boldsymbol{\tilde Q}}_1^{(0)} = { {\textit{0}}},{\boldsymbol{Q}}_2^{(0)} = {{\boldsymbol{I}}_N},P_1^{(0)} = 0.5$ , 　 ${{\boldsymbol{\varPhi }}^{(0)}} = {{\boldsymbol{\varPhi }}^{{\text{rand}}}},\varepsilon = {10^{ - 5}},t = 0$ ；
(2) 步骤1　给定 ${\boldsymbol{\varPhi }}$ ，求解 ${\boldsymbol{Q}}_1^{{\text{opt}}}$ , ${\boldsymbol{Q}}_2^{{\text{opt}}}$ ；
(a) 根据 ${\boldsymbol{Q}}_1^{(t - 1)}$ 和 $P_1^{(t - 1)}$ ，求解问题 $({\text{P}}3)$ 得到 ${\boldsymbol{Q}}_2^{(t)}$ ；
(b) 根据 ${\boldsymbol{\tilde Q}}_1^{(t - 1)}$ , ${\boldsymbol{Q}}_2^{(t)}$ 和 $P_1^{(t - 1)}$ ，求解问题 $({\text{P}}4)$ 得到 ${\boldsymbol{Q}}_1^{(t)}$ ；
(c) 记 ${\boldsymbol{Q}}_1^{{\text{opt}}} = {\boldsymbol{Q}}_1^{(t)}$ , ${\boldsymbol{Q}}_2^{{\text{opt}}} = {\boldsymbol{Q}}_2^{(t)}$ ；
(3) 步骤2　给定 ${{\boldsymbol{Q}}_1}$ , ${{\boldsymbol{Q}}_2}$ ，求解 ${{\boldsymbol{\varPhi }}^{{\text{opt}}}}$ ；
(a) 由 ${{\boldsymbol{\varPhi }}^{\left( {t - 1} \right)}}$ 以及式(35)计算 ${\nabla _{\boldsymbol{\theta}} }{\bar R^{(t - 1)}}_{{\text{sum}}}$ ；
(b) 由式(37)计算 ${{\boldsymbol{\theta }}^{(t)}}$ ，得 ${{\boldsymbol{\varPhi }}^{(t)}} = {\text{diag(}}{{\boldsymbol{\theta }}^{(t)}}{\text{)}}$
(c) 更新 $t = t + 1$ ；
(d) 循环步骤2直至速率增量满足 $\left\| \bar R_{{\text{sum}}}^{(t + 1)} - \bar R_{{\text{sum}}}^{(t)} \right\| < \varepsilon$ ；
(e) 记 ${{\boldsymbol{\varPhi }}^{{\text{opt}}}} = {{\boldsymbol{\varPhi }}^{\left( t \right)}}$ ；
(f) 根据 ${\boldsymbol{Q}}_1^{(t)}$ , ${\boldsymbol{Q}}_2^{(t)}$ 和 ${{\boldsymbol{\varPhi }}^{\left( t \right)}}$ ，求解问题 $({\text{P8}})$ 得到功率分配因子　　 $P_1^{(t)}$ ；
(g) 记 ${\boldsymbol{Q}}_1^{{\text{opt}}} = P_1^{(t)}{\boldsymbol{Q}}_1^{{\text{opt}}}$ 和 ${\boldsymbol{Q}}_2^{{\text{opt}}} = (P - P_1^{(t)}){\boldsymbol{Q}}_2^{{\text{opt}}}$ ；
(4) 循环步骤1～2直至 ${\bar R_{{\text{sum}}}}$ 增量小于 $\varepsilon$ ；
(5) 输出 ${\boldsymbol{Q}}_1^{{\text{opt}}}$ , ${\boldsymbol{Q}}_2^{{\text{opt}}}$ 和 ${{\boldsymbol{\varPhi }}^{{\text{opt}}}}$ 。

下载: 导出CSV

表 1 默认仿真参数设置

总功率 $P$	噪声功率	弱用户速率需求 ${R_0}$	角度拓展 ${\delta _i}$	平均角度	试验次数
30 dBm	–80 dBm	1.5 bps/Hz	${10^ \circ }$	$\begin{gathered} {\theta _{{T_1}}} = {\theta _{{T_2}}} = {0^ \circ },{\theta _{{T_3}}} = {10^ \circ } \\ {\theta _{{R_1}}} = {\theta _{{R_2}}} = - {5^ \circ },{\theta _{{R_3}}} = {5^ \circ } \\ \end{gathered}$	${10^4}$

下载: 导出CSV

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