Learning the wireless V2I channels using deep neural networks

Tian Hao Li*, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Risala T. Khan

*Corresponding author for this work

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

16 Citations (Scopus)
290 Downloads (Pure)


For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to- infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
ISBN (Electronic)9781728112206
Publication statusPublished - 7 Nov 2019
Event90th IEEE Vehicular Technology Conference 2019 - Honolulu, United States
Duration: 22 Sept 201925 Sept 2019

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252
ISSN (Electronic)2577-2465


Conference90th IEEE Vehicular Technology Conference 2019
Abbreviated titleVTC 2019 Fall
Country/TerritoryUnited States

ASJC Scopus subject areas

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


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