Channel Estimation based on Deep Learning in Vehicle-to-everything Environments

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalIEEE Communications Letters
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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