Deep-Learning Based Ship-Radiated Noise Suppression for Underwater Acoustic OFDM Systems

Lazar Atanackovic, Lutz Lampe, Roee Diamant

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


Interference due to ship-radiated noise in the underwater acoustic (UA) channel generates additive distortions that degrade wireless UA communications signals. Compressed sensing (CS) techniques are an approach used to estimate and suppress the impulsive components of ship-radiated noise for orthogonal frequency-division multiplexing (OFDM) systems by exploiting the null sub-carriers not used for data transmission. However, these CS-based estimation methods are constrained to estimating sparse signals and typically require slow iterative solvers. To combat these drawbacks, we propose a deep learning (DL) approach to structured signal recovery for estimating and mitigating the interfering effects of ship-radiated noise for OFDM systems. Our results indicate that the DL models, trained via publicly available long term acoustic data of shipping noise signals, produce measurable mitigation gains to the benchmark CS algorithms. In addition, we show the DL models outperform the benchmark CS estimation methods on new never before 'seen' experimentally acquired ship-radiated noise data.

Original languageEnglish
Title of host publication2020 Global Oceans 2020
Subtitle of host publicationSingapore - U.S. Gulf Coast
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154466
StatePublished - 5 Oct 2020
Event2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020 - Biloxi, United States
Duration: 5 Oct 202030 Oct 2020

Publication series

Name2020 Global Oceans 2020: Singapore - U.S. Gulf Coast


Conference2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


  • Deep learning (DL)
  • compressed sensing (CS)
  • deep neural network (DNN)
  • orthogonal frequency-division multiplexing (OFDM)
  • ship-radiated noise

ASJC Scopus subject areas

  • Oceanography
  • Automotive Engineering
  • Instrumentation
  • Signal Processing


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