Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning

Chaoyi Yang; Junlong Wang; Zhenni Pan; Shigeru Shimamoto

doi:10.1109/VTC2022-Fall57202.2022.10012759

Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning

Chaoyi Yang^*, Junlong Wang, Zhenni Pan, Shigeru Shimamoto

^*Corresponding author for this work

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

Abstract

In this work, a channel estimation method for OTFS using superimposing pilot is proposed. The pilot is superimposing on the first transmitter data symbol, yielding an enhanced frequency domain of Delay-Doppler domain pattern at the receiver end. A deep convolution neural network is proposed to de-noise the interfered channel matrix. Simulation results show that the bit error rate performance of the proposed method is better than that of the existing methods at low pilot energy.

Original language	English
Title of host publication	2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454681
DOIs	https://doi.org/10.1109/VTC2022-Fall57202.2022.10012759
Publication status	Published - 2022
Event	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom Duration: 2022 Sept 26 → 2022 Sept 29

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2022-September
ISSN (Print)	1550-2252

Conference

Conference	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/Territory	United Kingdom
City	London
Period	22/9/26 → 22/9/29

Keywords

channel estimation
machine learning
OTFS
ResNet
superimposing pilot

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTC2022-Fall57202.2022.10012759

Cite this

Yang, C., Wang, J., Pan, Z., & Shimamoto, S. (2022). Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings (IEEE Vehicular Technology Conference; Vol. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012759

Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning. / Yang, Chaoyi; Wang, Junlong; Pan, Zhenni et al.

2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE Vehicular Technology Conference; Vol. 2022-September).

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

Yang, C, Wang, J, Pan, Z & Shimamoto, S 2022, Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning. in 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. IEEE Vehicular Technology Conference, vol. 2022-September, Institute of Electrical and Electronics Engineers Inc., 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022, London, United Kingdom, 22/9/26. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012759

Yang C, Wang J, Pan Z , Shimamoto S. Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE Vehicular Technology Conference). doi: 10.1109/VTC2022-Fall57202.2022.10012759

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title = "Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning",

abstract = "In this work, a channel estimation method for OTFS using superimposing pilot is proposed. The pilot is superimposing on the first transmitter data symbol, yielding an enhanced frequency domain of Delay-Doppler domain pattern at the receiver end. A deep convolution neural network is proposed to de-noise the interfered channel matrix. Simulation results show that the bit error rate performance of the proposed method is better than that of the existing methods at low pilot energy.",

keywords = "channel estimation, machine learning, OTFS, ResNet, superimposing pilot",

author = "Chaoyi Yang and Junlong Wang and Zhenni Pan and Shigeru Shimamoto",

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AB - In this work, a channel estimation method for OTFS using superimposing pilot is proposed. The pilot is superimposing on the first transmitter data symbol, yielding an enhanced frequency domain of Delay-Doppler domain pattern at the receiver end. A deep convolution neural network is proposed to de-noise the interfered channel matrix. Simulation results show that the bit error rate performance of the proposed method is better than that of the existing methods at low pilot energy.

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Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning

Abstract

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Keywords

ASJC Scopus subject areas

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