可重构智能表面辅助通信系统时变级联信道估计

doi:10.11959/j.issn.1000-436x.2024028

通信学报 ›› 2024, Vol. 45 ›› Issue (1): 119-128.doi: 10.11959/j.issn.1000-436x.2024028

• 学术论文 • 上一篇

可重构智能表面辅助通信系统时变级联信道估计

邵凯¹^,²^,³, 鲁奔¹, 王光宇¹^,²

¹ 重庆邮电大学通信与信息工程学院，重庆 400065
² 移动通信技术重庆市重点实验室，重庆 400065
³ 移动通信教育部工程研究中心，重庆 400065

修回日期:2023-11-01 出版日期:2024-01-01 发布日期:2024-01-01
作者简介:邵凯（1977- ），男，重庆人，重庆邮电大学副教授、硕士生导师，主要研究方向为智能感知与信息系统、信号与信息智能处理等
鲁奔（1999- ），男，河南南阳人，重庆邮电大学硕士生，主要研究方向为智能反射面、信道估计
王光宇（1964- ），男，贵州贵阳人，重庆邮电大学教授、硕士生导师，主要研究方向为数字信号处理、滤波器组理论等
基金资助:
国家自然科学基金资助项目(U23A20279)

Time-varying channel estimation in reconfigurable intelligent surface assisted communication system

Kai SHAO¹^,²^,³, Ben LU¹, Guangyu WANG¹^,²

¹ School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
² Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China
³ Engineering Research Center of Mobile Communications of the Ministry of Education, Chongqing 400065, China

Revised:2023-11-01 Online:2024-01-01 Published:2024-01-01
Supported by:
The National Natural Science Foundation of China(U23A20279)

摘要/Abstract

摘要：

针对可重构智能表面（RIS）辅助通信系统时变级联信道的估计中需解决的级联信道稀疏表示、时变信道参数跟踪和信号重构等关键问题，提出了一种结合 Khatri-Rao 积的分层贝叶斯卡尔曼滤波（KR-HBKF）算法。该算法首先利用信道的稀疏特性，通过Khatri-Rao积和克罗内克积变换得到RIS级联信道的稀疏表示，将RIS级联信道估计问题转化为低维度的稀疏信号恢复问题。然后，根据RIS级联信道的状态演化模型，在HBKF算法的预测模型中引入了时间相关性参数，应用改进的HBKF解决时变信道参数跟踪和信号重构问题，完成时变级联信道的估计。KR-HBKF 算法综合利用了信道的稀疏性和时间相关性，能以较小的导频开销获得更好的估计精度。仿真结果表明，与传统压缩感知算法相比，所提算法具有约 5 dB的估计性能提升，且在不同的时变信道条件下具有更好的鲁棒性。

关键词: 可重构智能表面, 信道估计, 贝叶斯压缩感知, 卡尔曼滤波

Abstract:

Aiming at the key problems need to be solved, such as cascade channel sparse representation, time-varying channel parameter tracking and signal reconstruction, for time-varying cascade channels estimation of reconfigurable intelligent surface (RIS) assisted communication system, a Khatri-Rao and hierarchical Bayesian Kalman filter (KR-HBKF) algorithm was proposed.Firstly, the Khatri-Rao product and Kronecker product transformations were used to obtain the sparse representation of RIS cascaded channels based on the sparse characteristics of channels, thus the RIS cascaded channel estimation problem was transformed into a low-dimensional sparse signal recovery problem.Then, according to the state evolution model of RIS cascaded channel, the time correlation parameter was introduced into the prediction model of HBKF algorithm, and the improved HBKF was applied to solve the problem of time-varying channel parameter tracking and signal reconstruction for completing the time-varying cascaded channels estimation.The sparsity and time correlation of the channel were comprehensively considered in the KR-HBKF algorithm, thus better estimation accuracy could be obtained with small pilot overhead.Compared with the traditional compressed sensing algorithm, the simulation results show that the proposed algorithm has about 5 dB estimated performance improvement, and better robustness performance under different time-varying channel conditions.

Key words: reconfigurable intelligent surface, channel estimation, Bayesian compressed sensing, Kalman filter

中图分类号:

TN928

邵凯, 鲁奔, 王光宇. 可重构智能表面辅助通信系统时变级联信道估计[J]. 通信学报, 2024, 45(1): 119-128.

Kai SHAO, Ben LU, Guangyu WANG. Time-varying channel estimation in reconfigurable intelligent surface assisted communication system[J]. Journal on Communications, 2024, 45(1): 119-128.

图/表 6

图1

图2

图3

图4

图5

表1

算法复杂度对比"

算法	计算复杂度
OMP算法	$O (Q M M_{g} N_{g})$
KF算法	$O (Q M M_{g}^{2} N_{g}^{2})$
BCS算法	$O (M_{g}^{3} N_{g}^{3})$
KR-HBKF算法	$O (M_{g}^{3} N_{g}^{3})$

表1

参考文献 29

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可重构智能表面辅助通信系统时变级联信道估计

Time-varying channel estimation in reconfigurable intelligent surface assisted communication system

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