RIS辅助太赫兹频段车载网络容量优化

doi:10.11959/j.issn.1000-436x.2023176

通信学报 ›› 2023, Vol. 44 ›› Issue (10): 103-111.doi: 10.11959/j.issn.1000-436x.2023176

• 学术论文 • 上一篇

RIS辅助太赫兹频段车载网络容量优化

陈发堂, 刘小玲, 王丹, 张若凡

重庆邮电大学通信与信息工程学院，重庆 400065

修回日期:2023-09-01 出版日期:2023-10-01 发布日期:2023-10-01
作者简介:陈发堂（1965− ），男，重庆人，重庆邮电大学研究员、硕士生导师，主要研究方向为物理层算法、可重构智能反射面等
刘小玲（1998− ），女，重庆人，重庆邮电大学硕士生，主要研究方向为车联网物理层协议、可重构智能反射面等
王丹（1982− ），女，重庆人，博士，重庆邮电大学研究员、硕士生导师，主要研究方向为可重构智能反射面技术、嵌入式系统（移动通信基带处理系统）、通信软件开发等
张若凡（2001− ），男，湖北荆州人，重庆邮电大学硕士生，主要研究方向为车联网物理层协议、可重构智能反射面等
基金资助:
重庆市自然科学基金资助项目(cstc2021jcyj-msxmX0454)

RIS-assisted terahertz frequency band vehicle network capacity optimization

Fatang CHEN, Xiaoling LIU, Dan WANG, Ruofan ZHANG

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Revised:2023-09-01 Online:2023-10-01 Published:2023-10-01
Supported by:
The Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0454)

摘要/Abstract

摘要：

为缓解当前无线系统的频谱稀缺和容量限制，引入太赫兹频段并以可重构智能反射面（RIS）进行辅助通信构建一个下行链路车载网络。考虑系统总功率有限约束、蜂窝移动用户服务质量约束、车辆和用户位置随机性约束，以最大化车辆用户总速率建立最优功率分配和RIS最佳部署的混合优化模型。基于平衡法、线性变换法和消元法将原NP-hard问题转化为具有复相关约束多变量耦合的凸优化问题，内层迭代基于拉格朗日乘子法求解最优功率分配，外层迭代基于改进式遗传算法求解RIS最佳部署。仿真结果表明，基于最优功率分配下合理部署RIS节点个数与分布密度能在获得较高目标总容量的同时节约成本。

关键词: 可重构智能反射面, 太赫兹, 功率分配, 车联网

Abstract:

In order to alleviate the spectrum scarcity and capacity limitation of the current wireless system, the terahertz frequency band was introduced and the reconfigurable intelligent surface (RIS) was used for auxiliary communication to construct a downlink vehicle network.Considering the constraints of limited total system power, QoS constraints of cellular mobile users, and randomness constraints of vehicle and user locations, a hybrid optimization model was established for optimal power allocation and optimal deployment of RIS with the aim of maximizing the total rate of vehicle users.Based on the balance method, linear transformation method, and element elimination method, the original NP-hard problem was transformed into a convex optimization problem with complex correlation constraints and multivariate coupling.The inner layer iteration was based on the Lagrange multiplier method to solve the optimal power allocation, and the outer layer iteratively solved the optimal deployment of RIS based on a improved genetic algorithm.The simulation results show that rationally deploying the number and distribution density of RIS nodes based on optimal power allocation can save costs while achieving a higher target total capacity.

Key words: RIS, terahertz, power distribution, IoV

中图分类号:

TN92

陈发堂, 刘小玲, 王丹, 张若凡. RIS辅助太赫兹频段车载网络容量优化[J]. 通信学报, 2023, 44(10): 103-111.

Fatang CHEN, Xiaoling LIU, Dan WANG, Ruofan ZHANG. RIS-assisted terahertz frequency band vehicle network capacity optimization[J]. Journal on Communications, 2023, 44(10): 103-111.

图/表 8

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参数	含义
N	RIS的个数
f	THz载波频率
σ	噪声方差
R	TBS覆盖半径
Pmax	最大功率预算
SINR_min	满足QoS的最低阈值
α	路径损失指数
v	车辆用户速度
d	相关用户之间的距离
T	开尔文温度
ς	相对湿度
p	气压

RIS辅助太赫兹频段车载网络容量优化

RIS-assisted terahertz frequency band vehicle network capacity optimization

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