Joint TRFI and Deep Learning for Vehicular Channel Estimation

Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis

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

Abstract

IEEE 802.11p standard enables the wireless technology that defines vehicular communications. However, IEEE 802.11p frame structure employing low pilot density is not enough to track the channel variations in high mobility scenarios, leading to significant performance degradation. Therefore, ensuring communication reliability in vehicular environments is considered as a major challenge. In this work, this challenge is tackled by employing deep learning into conventional channel estimation through utilizing deep neural networks (DNN) as an additional non-linear processing unit to correct the interpolation error of the time domain reliable test frequency domain interpolation (TRFI) channel estimates, besides learning higher order statistics of the estimated channel, resulting in a better channel tracking over time. Simulation results demonstrate the performance superiority of the proposed TRFI-DNN scheme over conventional schemes and the recently proposed DNN estimators with a significant computational complexity decrease, especially in high mobility vehicular scenarios.

Original languageEnglish (US)
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
StatePublished - Dec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei
Period12/7/2012/11/20

Keywords

  • Channel estimation
  • deep learning
  • DNN
  • IEEE 802.11p standard
  • vehicular communications

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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