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

Abstract

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
DOIs
Publication statusPublished - 2019 Sep
Event90th IEEE Vehicular Technology Conference, VTC 2019 Fall - Honolulu, United States
Duration: 2019 Sep 222019 Sep 25

Publication series

NameIEEE Vehicular Technology Conference
Volume2019-September
ISSN (Print)1550-2252

Conference

Conference90th IEEE Vehicular Technology Conference, VTC 2019 Fall
CountryUnited States
CityHonolulu
Period19/9/2219/9/25

All Science Journal Classification (ASJC) codes

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

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    Lee, S., Jo, H., Mun, C., & Yook, J. G. (2019). Noise Suppression Chanel Estimation Method Using Deep Learning in IEEE 802.11p Standard. In 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings [8891554] (IEEE Vehicular Technology Conference; Vol. 2019-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCFall.2019.8891554