Full Text:   <58>

CLC number: 

On-line Access: 2024-05-01

Received: 2023-11-08

Revision Accepted: 2024-04-06

Crosschecked: 0000-00-00

Cited: 0

Clicked: 89

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300760


Deep unfolding-based channel estimation for wide band terahertz near-field massive MIMO systems


Author(s):  Jiabao GAO, Xiaoming CHEN, Geoffrey Ye LI

Affiliation(s):  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   gao_jiabao@zju.edu.cn, chen_xiaoming@zju.edu.cn, Geoffrey.Li@imperial.ac.uk

Key Words:  Terahertz, Massive MIMO, Channel estimation, Deep learning


Jiabao GAO, Xiaoming CHEN, Geoffrey Ye LI. Deep unfolding-based channel estimation for wide band terahertz near-field massive MIMO systems[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

@article{title="Deep unfolding-based channel estimation for wide band terahertz near-field massive MIMO systems",
author="Jiabao GAO, Xiaoming CHEN, Geoffrey Ye LI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300760"
}

%0 Journal Article
%T Deep unfolding-based channel estimation for wide band terahertz near-field massive MIMO systems
%A Jiabao GAO
%A Xiaoming CHEN
%A Geoffrey Ye LI
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300760

TY - JOUR
T1 - Deep unfolding-based channel estimation for wide band terahertz near-field massive MIMO systems
A1 - Jiabao GAO
A1 - Xiaoming CHEN
A1 - Geoffrey Ye LI
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300760


Abstract: 
The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant bandwidth and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing-based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequencydependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding-based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE