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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Bibliographic Details
Published in:IEEE Vehicular Networking Conference pp. 281 - 282
Main Authors: Oh, Kang-Hyun, Kim, Chang Hyun, Lim, Sungmook, Song, Changick
Format: Conference Proceeding
Language:English
Published: IEEE 29.05.2024
Subjects:
Channel estimation
DAE
Deep learning
Neural networks
Noise
Noise reduction
Simulation
Training
V2X
ISSN:2157-9865
Online Access:Get full text
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Summary: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.
ISSN:2157-9865
DOI:10.1109/VNC61989.2024.10575982