Poster: A Simple Deep Learning Based V2X Channel Estimation Scheme Using Denoising Autoencoder

To implement the V2X systems, reliable channel estimation is a major critical challenge due to the rapid time-varying characteristic of the vehicular channels. In this recent result paper, we propose a denoising autoencoder (DAE) based channel estimation scheme. The proposed scheme has a simple neur...

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Vydáno v:IEEE Vehicular Networking Conference s. 281 - 282
Hlavní autoři: Oh, Kang-Hyun, Kim, Chang Hyun, Lim, Sungmook, Song, Changick
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 29.05.2024
Témata:
Channel estimation
DAE
Deep learning
Neural networks
Noise
Noise reduction
Simulation
Training
V2X
ISSN:2157-9865
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Abstract To implement the V2X systems, reliable channel estimation is a major critical challenge due to the rapid time-varying characteristic of the vehicular channels. In this recent result paper, we propose a denoising autoencoder (DAE) based channel estimation scheme. The proposed scheme has a simple neural network structure and significantly improves the channel estimation accuracy by training the auto encoder such that it can remove the noise and distortions generated during the data pilot aided channel estimation process. Simulation results verify that the proposed DAE outperforms the conventional deep learning based channel estimation schemes.
AbstractList To implement the V2X systems, reliable channel estimation is a major critical challenge due to the rapid time-varying characteristic of the vehicular channels. In this recent result paper, we propose a denoising autoencoder (DAE) based channel estimation scheme. The proposed scheme has a simple neural network structure and significantly improves the channel estimation accuracy by training the auto encoder such that it can remove the noise and distortions generated during the data pilot aided channel estimation process. Simulation results verify that the proposed DAE outperforms the conventional deep learning based channel estimation schemes.
Author Oh, Kang-Hyun
Lim, Sungmook
Song, Changick
Kim, Chang Hyun
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  givenname: Chang Hyun
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  givenname: Sungmook
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  organization: Korea National University of Transportation,Department of Electronic Engineering,Chungju,Korea,27469
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  givenname: Changick
  surname: Song
  fullname: Song, Changick
  email: c.song@ut.ac.kr
  organization: Korea National University of Transportation,Department of Electronic Engineering,Chungju,Korea,27469
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Snippet To implement the V2X systems, reliable channel estimation is a major critical challenge due to the rapid time-varying characteristic of the vehicular channels....
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StartPage 281
SubjectTerms Channel estimation
DAE
Deep learning
Neural networks
Noise
Noise reduction
Simulation
Training
V2X
Title Poster: A Simple Deep Learning Based V2X Channel Estimation Scheme Using Denoising Autoencoder
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