车联网环境下可重构智能反射面辅助无线信道估计算法

曾嵘; 杭潇

doi:10.11959/j.issn.1000-436x.2022150

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车联网环境下可重构智能反射面辅助无线信道估计算法

学术论文 | 更新时间：2024-06-05

- 车联网环境下可重构智能反射面辅助无线信道估计算法
- Reconfigurable intelligent surface assist wireless channel estimation algorithm in Internet of vehicles environment
- 通信学报 2022年43卷第8期页码：142-150
- 作者机构：
- 作者简介：
  
  [ "曾嵘（1976- ），男，江苏淮安人，博士，杭州电子科技大学副教授，主要研究方向为移动通信系统中的物理层传输技术" ]
  [ "杭潇（1996- ），男，山东惠民人，杭州电子科技大学硕士生，主要研究方向为智能反射面场景下的信道估计技术" ]
- 基金信息：
  
  东南大学移动通信国家重点实验室开放研究基金资助项目;The Open Research Fund of the State Key Laboratory of Mobile Communications of Southeast University(2020D-13);之江实验室开放课题基金资助项目;The Open Research Fund of Zhijiang Laboratory(2019LC0AB02)
- DOI：10.11959/j.issn.1000-436x.2022150
  中图分类号： TN92
- 网络首发：2022-08，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
曾嵘, 杭潇. 车联网环境下可重构智能反射面辅助无线信道估计算法[J]. 通信学报, 2022,43(8):142-150.

Rong ZENG, Xiao HANG. Reconfigurable intelligent surface assist wireless channel estimation algorithm in Internet of vehicles environment[J]. Journal on Communications, 2022, 43(8): 142-150.
曾嵘, 杭潇. 车联网环境下可重构智能反射面辅助无线信道估计算法[J]. 通信学报, 2022,43(8):142-150. DOI： 10.11959/j.issn.1000-436x.2022150.

Rong ZENG, Xiao HANG. Reconfigurable intelligent surface assist wireless channel estimation algorithm in Internet of vehicles environment[J]. Journal on Communications, 2022, 43(8): 142-150. DOI： 10.11959/j.issn.1000-436x.2022150.

摘要

针对上行链路车联网环境下可重构智能反射面（RIS）辅助多用户的信道估计问题，提出了一种基于位置信息辅助的压缩感知信道估计算法。首先，基于通信设备位置信息，搭建单RIS辅助单用户通信模型，根据波束出发角（AOD）与到达角（AOA）的逻辑关系推导了最优相移矩阵，然后根据获得的相移矩阵，基于压缩感知理论构建了感知矩阵并进行信道估计，最后扩展到多用户场景下迭代求解。基于车联网技术获得的位置信息求解最优RIS相移矩阵，减少了信道额外的训练开销，进一步降低了信道估计的复杂度。仿真结果表明，所提算法具有较高的信道估计性能。

Abstract

Aiming at the problem that multi-user channel estimation assisted by reconfigurable intelligent surface (RIS) in the uplink Internet of vehicles environment

a location assisted compressive sensing channel estimation algorithm was proposed.Based on the location information of the communication equipment

a single RIS-assisted single-user communication model was built

and the optimal phase shift matrix was derived according to the logical relationship between the beam angle of departure (AOD) and the angle of arrival (AOA).The perception matrix was constructed and channel estimation was performed

and finally it was extended to multi-user scenarios and solved iteratively.The optimal RIS phase shift matrix was solved based on the position information obtained by the Internet of vehicles technology

which reduced the additional training overhead of the channel and further reduces the complexity of the channel estimation.Simulation results show that the proposed algorithm based on location information has high channel estimation performance.

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references

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