A low complexity OMP sparse channel estimation algorithm in OFDM system

Shaochen Zhang, Lijun Xu, Shefeng Yan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Channel estimation is the key to underwater acoustic OFDM systems. Considering the sparsity of the underwater acoustic channel, compressed sensing is used for channel estimation. However, many existing algorithms are either too complicated or cannot guarantee the accuracy of reconstruction. Therefore, a fast twice orthogonal matching pursuit (TOMP) channel estimation algorithm is proposed. The orthogonality of some measurement atoms is exploited to eliminate the iteration steps in the first OMP, and the number of measurement atoms is reduced according to the relationship between the different over-sampling factors of the measurement matrix in the second OMP. Compared with the conventional OMP algorithm, TOMP can reduce by about 2/3 computational complexity, and maintain accuracy in the sparse underwater acoustic channel estimation. Both the simulation and the sea experiment in the south China sea results show the feasibility of proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665429184
DOIs
Publication statusPublished - 17 Aug 2021
Event2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 - Xi�an, China
Duration: 17 Aug 202119 Aug 2021

Publication series

NameProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

Conference

Conference2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
Country/TerritoryChina
CityXi�an
Period17/08/2119/08/21

Keywords

  • OFDM
  • OMP algorithm
  • Sparse channel estimation
  • compressive sensing
  • low complexity

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