可重构智能表面辅助的V2I通信系统联合波束赋形算法

仲伟志; 何艺; 段洪涛; 万诗晴; 范振雄; 朱秋明; 林志鹏

doi:10.11999/JEIT231324

可重构智能表面辅助的V2I通信系统联合波束赋形算法

doi: 10.11999/JEIT231324

1.
南京航空航天大学电磁频谱空间认知动态系统工业与信息化部重点实验室南京 211106
2.
国家无线电监测中心北京 100144

基金项目: 江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022067, BE2022067-1, BE2022067-3)，国家自然科学基金(62271250)，南京航空航天大学研究生科研与实践创新计划(xcxjh20231507)

详细信息

作者简介:
仲伟志：女，副教授，研究方向为5G中的毫米波通信、波束成形、波束跟踪技术和无人机轨迹规划

何艺：女，硕士生，研究方向为车载毫米波通信，可重构智能表面联合波束赋形

段洪涛：男，正高级工程师，研究方向为无人机通信与反制，频谱管理，短波及超短波监测等

万诗晴：女，硕士生，研究方向为无人机通信，可重构智能表面联合波束赋形

范振雄：男，高级工程师，研究方向为无人机通信与反制，超短波干扰定位及查找，短波测向等

朱秋明：男，教授，研究方向为电磁信号传播机理、无线信道测量建模应用、电磁频谱语义孪生以及通信装备智能化测试等

林志鹏：男，副研究员，研究方向为高维信道参数估计、大规模阵列信号处理、无人机通信、频谱信号感知及重构等

通讯作者:
仲伟志　zhongwz@nuaa.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2023-11-30
- 修回日期: 2024-06-14
- 网络出版日期: 2024-06-21

Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

1.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
State Radio Monitoring Center, Beijing 100144, China

Funds: Primary Research and Development Plan of Jiangsu Province (Industry foresight and key core technology)(BE2022067, BE2022067-1, BE2022067-3), The National Natural Science Foundation of China (62271250), Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

摘要

摘要: 为解决基于信道先验知识的联合波束赋形方法受限于多变的车辆与交通基础设施(V2I)通信场景且信道估计开销过大等问题，该文结合环境态势感知，提出一种基于无线传播链路预测的联合波束赋形方法。该方法首先利用射线追踪模拟器构建了可重构智能表面(RIS)辅助的V2I毫米波通信系统模型，通过改变环境态势以获取多样的无线传播链路数据来构建数据集。其次，使用该数据集训练基于机器学习的无线传播链路预测模型。最后，在最大发射功率约束条件下，构建了联合波束赋形问题模型，并基于预测结果采用交替迭代优化方法(AIOA)优化基站波束赋形矩阵和RIS相移矩阵，以实现同步通信车辆用户最小信干噪比(SINR)的最大化。仿真结果验证了该方法的有效性，通过引入非信道先验知识驱动，降低了信道探测开销，提高了该方法在V2I场景中的可行性。
- V2I通信 /
- 可重构智能表面 /
- 联合波束赋形 /
- 环境态势感知 /
- 信道预测
Abstract: In order to address the limitations of the joint beamforming method based on channel prior knowledge, which is constrained by multivariate Vehicle-to-Infrastructure (V2I) communication scenes and suffers from large overhead caused by channel estimation, a wireless propagation link prediction-based joint beamforming method assisted by environmental situation awareness is proposed in this paper. Firstly, a model of Reconfigurable Intelligent Surface (RIS) assisted mmWave communication system for V2I networks is established using a ray tracer. To build a dataset, diverse data of wireless propagation links is obtained by changing the environmental situation. Then, this dataset is used to train a machine learning-based wireless propagation link prediction model. Finally, the joint beamforming problem under the constraint of maximum transmission power is modeled. Additionally, based on the prediction outcome, the beamforming matrix of base station and the phase shift matrix of RIS are optimized using Alternating Iterative Optimization Algorithm (AIOA) to maximize the minimum Signal to Interference plus Noise Ratio (SINR) among synchronous communication vehicle users. Simulation results validate the effectiveness of our method. Introducing non-channel prior knowledge driven reduces channel detection overhead and improves feasibility in applying our method to V2I scenes.
- Vehicle-to-Infrastructure(V2I) communication /
- Reconfigurable Intelligent Surface(RIS) /
- Joint beamforming /
- Environmental situation awareness /
- Channel prediction

HTML全文

图 1 RIS辅助的V2I多用户毫米波通信系统示意图

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图 2 环境态势感知的实现方法示意图

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图 3 坐标系示意图

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图 4 场景模型以及无线传播链路可视化结果

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图 5 基于无线传播链路预测的联合波束赋形方法流程图

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图 6 交替迭代优化算法流程图

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图 7 不同迭代停止精度$ {\beta _2} $下的AIOA性能比较

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图 8 不同维度RIS的AIOA性能比较

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图 9 基于不同编码方式预测结果的AIOA性能比较

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图 10 不同联合波束赋形方法的$ {\text{E}}[\min ({\text{SIN}}{{\text{R}}_k})] $性能比较

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图 11 基于真实信道和预测信道的AIOA总频带利用率比较

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图 12 定位误差的影响

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图 13 不同车辆用户数对联合波束赋形性能的影响

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1 二分搜索算法

初始化参数：$ {\beta _1} $, $ P $, $ \delta $, $ {\boldsymbol{\varTheta}} $,$ {\lambda _{\text{u}}} $, $ {\lambda _{\text{l}}} = 0 $
(1) While $ ({\lambda _{\text{u}}} - {\lambda _{\text{l}}}) \ge {\beta _1} $ do
(2) 　$ t = ({\lambda _{\text{u}}}{\text{ + }}{\lambda _{\text{l}}})/2 $
(3) 　利用半定松弛方法解决问题(P2a)
(4) 　if 当前的$ t $和$ \Theta $可以使得问题(P2a)状态为可行 then
(5) 　　$ {\lambda _{\text{u}}} = \delta t,{\text{ }}{\lambda _{\text{l}}} = t $
(6) 　else
(7) 　　$ {\lambda _{\text{u}}} = t $
(8) 　end if
(9) end while
输出$ t;{\text{ }}{\boldsymbol{X}}_k^{{\text{opt}}},\forall k = \{ 1,2, \cdots ,K\} $

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表 1 通用仿真参数

参数	值	参数	值
M	9	单车道尺寸/(长, 宽)(m)	(100, 5)
N	{16, 36}	BS坐标/(水平位置, 高度)(m)	(50, 3)
L	2	RIS坐标/(水平位置, 高度)(m)	(50, 8)
K	2	小型车辆尺寸/(长, 宽, 高)(m)	(4, 2, 1.5)
$ \sigma _k^2 $(dB)	–110	大型车辆尺寸/(长, 宽, 高)(m)	(9, 2.5, 2.5)
$ \kappa $(dB)	4	信号中心频率(GHz)	28

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表 2 不同编码方式对应的环境态势特征下预测模型在测试集上的MSE

	T_1,1	T_2,1	T_3,1	T_4,1	T_5,1	T_1,2	T_2,2	T_3,2	T_4,2	T_5,2
TVOM	3.07E-07	4.50E-07	2.82E-04	1.71E-02	1.44E-06	7.07E-04	1.24E-03	5.75E-03	2.18E-02	3.25E-03
NOCM	1.86E-07	1.96E-07	9.45E-07	3.96E-05	1.21E-06	3.66E-04	8.60E-04	2.73E-04	4.62E-04	1.55E-03
OLCM,N_v=2	1.22E-07	8.66E-09	7.14E-08	4.04E-07	3.92E-07	2.66E-04	7.75E-04	4.59E-04	5.11E-05	2.13E-03

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