Adaptive Channel Estimation based on Deep Learning

Abdul Karim Gizzini; Marwa Chafii; Ahmad Nimr; Gerhard Fettweis

doi:10.1109/VTC2020-Fall49728.2020.9348501

Adaptive Channel Estimation based on Deep Learning

Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis

Computer Engineering

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

Abstract

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

Original language	English (US)
Title of host publication	2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728194844
DOIs	https://doi.org/10.1109/VTC2020-Fall49728.2020.9348501
State	Published - Nov 2020
Event	92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada Duration: Nov 18 2020 → …

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2020-November
ISSN (Print)	1550-2252

Conference

Conference	92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
Country/Territory	Canada
City	Virtual, Victoria
Period	11/18/20 → …

Keywords

Adaptive channel estimation
Deep learning
LMMSE
Machine learning

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTC2020-Fall49728.2020.9348501

Cite this

Gizzini, A. K., Chafii, M., Nimr, A., & Fettweis, G. (2020). Adaptive Channel Estimation based on Deep Learning. In 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings Article 9348501 (IEEE Vehicular Technology Conference; Vol. 2020-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348501

Adaptive Channel Estimation based on Deep Learning. / Gizzini, Abdul Karim; Chafii, Marwa; Nimr, Ahmad et al.
2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9348501 (IEEE Vehicular Technology Conference; Vol. 2020-November).

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

Gizzini, AK, Chafii, M, Nimr, A & Fettweis, G 2020, Adaptive Channel Estimation based on Deep Learning. in 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings., 9348501, IEEE Vehicular Technology Conference, vol. 2020-November, Institute of Electrical and Electronics Engineers Inc., 92nd IEEE Vehicular Technology Conference, VTC 2020-Fall, Virtual, Victoria, Canada, 11/18/20. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348501

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abstract = "Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.",

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author = "Gizzini, {Abdul Karim} and Marwa Chafii and Ahmad Nimr and Gerhard Fettweis",

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AB - Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

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Adaptive Channel Estimation based on Deep Learning

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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