Channel Estimation Based on Deep Learning in Vehicle-to-Everything Environments

Jing Pan, Hangguan Shan, Rongpeng Li, Yingxiao Wu, Weihua Wu, Tony Q.S. Quek

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp the complicated variations of channel with limited number of pilots in the IEEE 802.11p systems, data pilot-aided (DPA) channel estimation has been widely studied. However, the error propagation in the DPA procedure, caused by the noise and the channel variation within adjacent symbols, limits the performance seriously. In this letter, we propose a deep learning based channel estimation scheme, which exploits a long short-term memory network followed by a multilayer perceptron network to solve the error propagation issue. Simulation results show that the proposed scheme outperforms currently widely-used DPA schemes for the IEEE 802.11p-based V2X communications.

原文English
文章編號9355192
頁(從 - 到)1891-1895
頁數5
期刊IEEE Communications Letters
25
發行號6
DOIs
出版狀態Published - 2021 6月

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 電腦科學應用
  • 電氣與電子工程

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