Channel Estimation Algorithm for RIS-Assisted High-Speed Railway Millimeter-Wave Communication

Yunbo Gao; Mingjie Wang; Jianli Xie; Yuzhe Wang; Yuhao Ban

doi:10.3788/LOP232405

Laser & Optoelectronics Progress, Volume. 61, Issue 17, 1706005(2024)

Channel Estimation Algorithm for RIS-Assisted High-Speed Railway Millimeter-Wave Communication

Yunbo Gao^1,2, Mingjie Wang^1、*, Jianli Xie³, Yuzhe Wang¹, and Yuhao Ban¹

Author Affiliations

¹School of Automation and Electrical Engineering, Lanzhou Jiaotong University,Lanzhou 730070, Gansu, China

²Key Lab of Opt-Electronic Technology and Intelligent Control of Minisitry of Education, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

³School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(7)

Fig. 1. System model of high-speed train running in free space

Download full size

Fig. 2. Channel transmission mechanism

Download full size

Fig. 3. Comparison chart of different experiment times. (a) Experiment 10 times; (b) experiment 100 times; (c) experiment 500 times; (d) experiment 1000 times

Download full size

Fig. 4. Comparison of NMSE changes with time for different channel estimation algorithms

Download full size

Fig. 5. Comparison of NMSE changes with SNR for different channel estimation algorithms

Download full size

Table 1. Kalman filter

View table

Table 1. Kalman filter

Algorithm 1. Kalman filter

1）Initialization：

Initialize ${\hat{H}}_{0} = 0$ ，and ${\hat{C}}_{0} = M I_{N}$

2）Prediction：

Use ${\hat{H}}_{i - 1}$ to predict the cascaded CSI in the $i$ th slot，i.e.

${\tilde{H}}_{i} = α {\hat{H}}_{i - 1} + d i a g (v_{i - 1}) h_{B R}^{*}$

Calculate the prior covariance，that is，

${\tilde{C}}_{i} = α^{2} {\hat{C}}_{i - 1} + M (1 - α^{2}) I_{N}$

3）Optimization：

Calculate the Kalman gain matrix：

$K_{i} = \tilde{C} Ψ_{i}^{*} (Ψ_{i}^{T} {\tilde{C}}_{i} Ψ_{i}^{*} + M I_{T_{P}})^{- 1}$

Calculate the posterior estimate：

${\hat{H}}_{i} = {\tilde{H}}_{i} + K_{i} (Y_{i}^{T} - Ψ_{i}^{T} {\tilde{H}}_{i})$

Calculate the posterior covariance：

${\hat{C}}_{i} = (I - K_{i} Ψ_{i}^{T}) {\tilde{C}}_{i}$

Table 2. Simulation parameter setting

View table

Table 2. Simulation parameter setting

Parameter	Symbol	Numerical value
Train speed /km/h	v	260
Number of BS antenna	N	8
Number of MR antenna	Q	1
Carrier frequency /GHz	$f_{c}$	28
Bandwidth /GHz	B	1.25
Transmitting power /dBm	P	30
Noise power spectrum /dBm	$σ^{2}$	-80
BS-MR path loss factor	${P_{P L}}_{0}$	3.5
BS-RIS path loss factor	$P_{P L 1}$	2.0
RIS-MR path loss factor	${P_{P L}}_{2}$	2.8

Tools

Get Citation

Copy Citation Text

Yunbo Gao, Mingjie Wang, Jianli Xie, Yuzhe Wang, Yuhao Ban. Channel Estimation Algorithm for RIS-Assisted High-Speed Railway Millimeter-Wave Communication[J]. Laser & Optoelectronics Progress, 2024, 61(17): 1706005

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Fiber Optics and Optical Communications

Received: Nov. 1, 2023

Accepted: Dec. 18, 2023

Published Online: Sep. 14, 2024

The Author Email: Mingjie Wang (2317247856@qq.com)

DOI:10.3788/LOP232405

CSTR:32186.14.LOP232405

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology