Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning

Chaoyi Yang*, Junlong Wang, Zhenni Pan, Shigeru Shimamoto

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

In this work, a channel estimation method for OTFS using superimposing pilot is proposed. The pilot is superimposing on the first transmitter data symbol, yielding an enhanced frequency domain of Delay-Doppler domain pattern at the receiver end. A deep convolution neural network is proposed to de-noise the interfered channel matrix. Simulation results show that the bit error rate performance of the proposed method is better than that of the existing methods at low pilot energy.

Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454681
DOIs
Publication statusPublished - 2022
Event96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom
Duration: 2022 Sept 262022 Sept 29

Publication series

NameIEEE Vehicular Technology Conference
Volume2022-September
ISSN (Print)1550-2252

Conference

Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom
CityLondon
Period22/9/2622/9/29

Keywords

  • channel estimation
  • machine learning
  • OTFS
  • ResNet
  • superimposing pilot

ASJC Scopus subject areas

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

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