Deep learning based channel covariance matrix estimation with scene images

Weihua Xu; Feifei Gao; Jianhua Zhang; Xiaoming Tao; Ahmed Alkhateeb; Shaodan Ma

doi:10.1109/ICCC52777.2021.9580233

Deep learning based channel covariance matrix estimation with scene images

Weihua Xu, Feifei Gao, Jianhua Zhang, Xiaoming Tao, Ahmed Alkhateeb, Shaodan Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from the environmental images and user speed, where the environmental images can reflect the user location information. Simulation results show that the proposed method is effective and will benefit the subsequent channel estimation.

Original language	English (US)
Title of host publication	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	162-166
Number of pages	5
ISBN (Electronic)	9781665443852
DOIs	https://doi.org/10.1109/ICCC52777.2021.9580233
State	Published - Jul 28 2021
Externally published	Yes
Event	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021 - Xiamen, China Duration: Jul 28 2021 → Jul 30 2021

Publication series

Name	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

Conference

Conference	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021
Country/Territory	China
City	Xiamen
Period	7/28/21 → 7/30/21

Keywords

Covariance estimation
Deep learning
Pilot free
Scene image

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems and Management
Safety, Risk, Reliability and Quality
Instrumentation

Access to Document

10.1109/ICCC52777.2021.9580233

Cite this

Xu, W., Gao, F., Zhang, J., Tao, X., Alkhateeb, A., & Ma, S. (2021). Deep learning based channel covariance matrix estimation with scene images. In 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021 (pp. 162-166). (2021 IEEE/CIC International Conference on Communications in China, ICCC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC52777.2021.9580233

Deep learning based channel covariance matrix estimation with scene images. / Xu, Weihua; Gao, Feifei; Zhang, Jianhua et al.
2021 IEEE/CIC International Conference on Communications in China, ICCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 162-166 (2021 IEEE/CIC International Conference on Communications in China, ICCC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xu, W, Gao, F, Zhang, J, Tao, X, Alkhateeb, A & Ma, S 2021, Deep learning based channel covariance matrix estimation with scene images. in 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021. 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 162-166, 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021, Xiamen, China, 7/28/21. https://doi.org/10.1109/ICCC52777.2021.9580233

Xu W, Gao F, Zhang J, Tao X, Alkhateeb A, Ma S. Deep learning based channel covariance matrix estimation with scene images. In 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 162-166. (2021 IEEE/CIC International Conference on Communications in China, ICCC 2021). doi: 10.1109/ICCC52777.2021.9580233

@inproceedings{d9b23de788984e008e0b1bb6d40f0b04,

title = "Deep learning based channel covariance matrix estimation with scene images",

abstract = "Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from the environmental images and user speed, where the environmental images can reflect the user location information. Simulation results show that the proposed method is effective and will benefit the subsequent channel estimation.",

keywords = "Covariance estimation, Deep learning, Pilot free, Scene image",

author = "Weihua Xu and Feifei Gao and Jianhua Zhang and Xiaoming Tao and Ahmed Alkhateeb and Shaodan Ma",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021 ; Conference date: 28-07-2021 Through 30-07-2021",

year = "2021",

month = jul,

day = "28",

doi = "10.1109/ICCC52777.2021.9580233",

language = "English (US)",

series = "2021 IEEE/CIC International Conference on Communications in China, ICCC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "162--166",

booktitle = "2021 IEEE/CIC International Conference on Communications in China, ICCC 2021",

}

TY - GEN

T1 - Deep learning based channel covariance matrix estimation with scene images

AU - Xu, Weihua

AU - Gao, Feifei

AU - Zhang, Jianhua

AU - Tao, Xiaoming

AU - Alkhateeb, Ahmed

AU - Ma, Shaodan

PY - 2021/7/28

Y1 - 2021/7/28

N2 - Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from the environmental images and user speed, where the environmental images can reflect the user location information. Simulation results show that the proposed method is effective and will benefit the subsequent channel estimation.

AB - Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from the environmental images and user speed, where the environmental images can reflect the user location information. Simulation results show that the proposed method is effective and will benefit the subsequent channel estimation.

KW - Covariance estimation

KW - Deep learning

KW - Pilot free

KW - Scene image

UR - http://www.scopus.com/inward/record.url?scp=85119321955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85119321955&partnerID=8YFLogxK

U2 - 10.1109/ICCC52777.2021.9580233

DO - 10.1109/ICCC52777.2021.9580233

M3 - Conference contribution

AN - SCOPUS:85119321955

T3 - 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

SP - 162

EP - 166

BT - 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

Y2 - 28 July 2021 through 30 July 2021

ER -

Deep learning based channel covariance matrix estimation with scene images

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this