智能反射面辅助无线网络性能及最优位置分析

束锋; 赖斯豪; 刘川; 高炜; 董榕恩; 王艳

doi:10.11999/JEIT240488

智能反射面辅助无线网络性能及最优位置分析

doi: 10.11999/JEIT240488

束锋^{1, 2},
赖斯豪¹,
刘川³,
高炜¹,
董榕恩^1, ,,
王艳¹

1.
海南大学信息与通信工程学院海口 570228
2.
南京理工大学电子工程与光电技术学院南京 210094
3.
国网智能电网研究院有限公司北京 102209

基金项目: 国家重点研发计划(2023YFF0612900)，国家自然科学基金(U22A2002, 62071234)，海南省科技专项基金(ZDKJ2021022)，海南大学科研启动项目(KYQD(ZR)-21008)，海南大学信息技术协同创新中心项目(XTCX2022XXC07)

详细信息

作者简介:
束锋：男，博士生导师，研究方向为智能无线通信、信息安全、大规模MIMO测向等

赖斯豪：男，硕士生，研究方向为IRS辅助的无线通信

刘川：男，博士，研究方向为能源数字化、电力通信网路

高炜：男，博士后，网络架构、无线网络接入和无线电资源分配

董榕恩：女，博士，研究方向为方向调制、IRS辅助的无线网络

王艳：女，博士生，研究方向为IRS辅助的通信系统

通讯作者:
董榕恩　dre2000@163.com

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2024-06-16
- 修回日期: 2024-09-24
- 网络出版日期: 2024-09-28

Performance and Optimal Placement Analysis of Intelligent Reflecting Surface-assisted Wireless Networks

SHU Feng^{1, 2},
LAI Sihao¹,
LIU Chuan³,
GAO Wei¹,
DONG Rongen^{1
, ,},
WANG Yan¹

1.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
2.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3.
State Grid Smart Grid Research Institute Co., Ltd., Beijing 102209, China

Funds: The National Key Research and Development Program of China (2023YFF0612900), The National Natural Science Foundation of China (U22A2002, 62071234), Hainan Province Science and Technology Special Fund (ZDKJ2021022), The Scientific Research Fund Project of Hainan University(KYQD(ZR)-21008), The Collaborative Innovation Center of Information Technology, Hainan University (XTCX2022XXC07)

摘要

摘要: 当基站(BS)和用户的位置固定，基站到智能反射面(IRS)与IRS到用户的距离和一定时，该文在视距信道和瑞利信道下基于最大化系统可达速率准则对无源和有源IRS的最优放置位置进行分析。首先，运用相位对齐和大数定律推导了无源和有源IRS辅助无线网络可达速率的闭合表达式；然后，分析了基站到IRS的路径损耗指数${\beta _1}$和IRS到用户的路径损耗指数${\beta _2}$对IRS最优部署位置的影响，即当${\beta _{\text{1}}} \gt {\beta _{\text{2}}}$时，无源IRS的最优部署位置始终靠近基站，随着${\beta _1}$和${\beta _2}$的差距逐渐增大，有源IRS的最优部署位置逐渐靠近基站；当${\beta _1} \lt {\beta _2}$时，则得到相反的结论。仿真结果表明：当${\beta _1} = {\beta _2}$且无源IRS到基站和到用户的距离相等时，系统的可达速率性能最差。当固定有源IRS处的噪声功率且增加用户处的噪声功率时，IRS的最优部署位置始终靠近用户；当固定后者增大前者时，IRS的最优部署位置逐渐靠近基站。
- 智能反射面 /
- 大数定律 /
- 智能反射面最优位置 /
- 可达速率
Abstract: When the locations of the Base Station (BS) and user are fixed and the sum of the distances from BS to the Intelligent Reflecting Surface (IRS) and from the IRS to the user is given, the optimal placement of passive and active IRSs based on the maximizing achievable rate criterion under line-of-sight and Rayleigh channels are analyzed in this paper. First, the phase alignment and the law of large numbers are employed to derive the close-form expressions of the achievable rates of passive and active IRS-assisted wireless networks. Then, the effects of the path loss exponent ${\beta _1}$ from the BS to IRS and the path loss exponent ${\beta _2}$ from the IRS to user on the optimal placement location of the IRS are analyzed. That is, when ${\beta _1} \gt {\beta _2}$, the optimal placement location of passive IRS is always close to the BS, and with the difference between ${\beta _1}$ and ${\beta _2}$ gradually increasing, the optimal placement location of active IRS is gradually close to the BS. The contrary conclusions are obtained when${\beta _1} < {\beta _2}$. Simulation results show that the achievable rate is worst when ${\beta _1} = {\beta _2}$ and the passive IRS is located at equal distances to the BS and user. When fixing the noise power at active IRS and increasing the noise power at user, the optimal placement location of active IRS is always close to the user. When fixing the latter and increasing the former, the optimal placement location of active IRS is gradually closer to the BS.
- Intelligent Reflecting Surface (IRS) /
- Law of large numbers /
- Optimal placement of IRS /
- Achievable rate

HTML全文

图 1 系统模型图

下载: 全尺寸图片幻灯片

图 2 系统可达速率与IRS部署位置的关系曲线图

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图 3 IRS最优部署位置与路径损耗曲线图

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图 4 有源IRS的最优部署位置随反射功率${P_{\text{I}}}$变化曲线图

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图 5 在不同$\sigma _{\text{I}}^2$和$\sigma _{\text{U}}^2$情形下系统可达速率随基站到有源IRS的距离$r$变化的关系曲线图。

下载: 全尺寸图片幻灯片

表 1 路径损耗指数设置表

	无源IRS		有源IRS
	${\beta _1}$	${\beta _2}$	${\beta _1}$	${\beta _2}$
${\beta _1} \gt {\beta _2}$	3.0	2.2	3.5	2.5
${\beta _1} = {\beta _2}$	2.2	2.2	2.5	2.5
${\beta _1} \lt {\beta _2}$	2.2	3.0	2.5	3.5

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