Deep Residual Learning for OTFS Channel Estimation with Arbitrary Noise

Xiaoqi ZHANG*, Weijie YUAN*, Chang LIU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)

Abstract

Orthogonal time frequency space (OTFS) modu-lation has proved its capability of achieving significant error performance advantages over orthogonal frequency division mul-tiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS channel estimation is that the performance of model-based estimators will drop dramatically in the scenarios with unknown and burst noise. In this paper, we model the channel estimation as a denoising problem and adopt a deep residual denoising network (DRDN) approach to implicitly learn the residual noise for recovering the channel matrix. Different from existing model-based channel estimators which only work well under white Gaussian noise, our proposed DRDN-based method is able to handle arbitrary noise, including both the correlated Gaussian noise and the non-Gaussian noise (e.g., t-distribution noise) cases. Finally, our simulations verify the effectiveness of the proposed OTFS channel estimation approach in arbitrary noise environments.

Original languageEnglish
Title of host publication2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022
PublisherIEEE
Pages320-324
Number of pages5
ISBN (Electronic)9781665459778
ISBN (Print)9781665459785
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022 - Sanshui, Foshan, China
Duration: 11 Aug 202213 Aug 2022

Conference

Conference2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022
Country/TerritoryChina
CitySanshui, Foshan
Period11/08/2213/08/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

This paper is supported by the National Natural Science Foundation of China under Project 62101232 and by the Natural Science Foundation of Guangdong Province under Grant 2022A1515011257.

Keywords

  • channel estimation
  • convolutional residual neural network
  • data denoising
  • orthogonal time frequency space (OTFS)

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