Noise Suppression Chanel Estimation Method Using Deep Learning in IEEE 802.11p Standard

Sangheon Lee, Hanshin Jo, Cheol Mun, Jong Gwan Yook

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a channel estimation method based on a complex valued regression of the neural network for the IEEE 802.11p standard. It consists of the complex weighted summation optimized by feedforward neural network with backpropagation algorithm using initial estimated channel of the pilot and the long preamble. It also exploits the shift matrix in order to mitigate the effect from a systemic problems in IEEE 802.11p standards. The major problems of IEEE 802.11p standard are wide bandwidth of 10 MHz consisting of 64 subcarriers and relatively insufficient four pilot subcarriers at single ODFM symbol, which are unsuitable for a channel of vehicular environment. Despite these problems, the proposed method performs better than the conventional channel estimation methods. The performance of proposed scheme is provided with the comparison between constructed data pilots (CDP), Spectral Temporal Averaging (STA), and proposed scheme. The proposed channel estimation scheme has low mean square error (MSE) and bit error rate (BER) throughout the whole SNR region. It is the result from properly trained weight. At the low SNR region, especially, the performance of proposed scheme is much better than CDP and STA scheme. It is because of the noise suppression effect caused by a weighted summation algorithm.

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112206
Publication statusPublished - 2019 Sept
Event90th IEEE Vehicular Technology Conference, VTC 2019 Fall - Honolulu, United States
Duration: 2019 Sept 222019 Sept 25

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference90th IEEE Vehicular Technology Conference, VTC 2019 Fall
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This research was supported by a grant (19CTAP-C151968-01) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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