Application of Machine Learning to Signal Detection in Underwater Wireless Optical Communication Links

Mohamed Nennouche, Mohammad Ali Khalighi, Alexis Dowhuszko, Djamal Merad, Jean Marc Boi

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

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Abstract

We consider the application of a machine-learning (ML)-based method to the demodulation of the received signal in underwater wireless optical communication (UWOC) links. This approach is justified when the underwater optical channel is subject to strong variations due to various phenomena such as pointing errors and turbulences, which directly impact the received optical power, requiring accurate and agile channel estimation. The investigated ML method is based on the well-known K-nearest neighbors (KNN). We demonstrate excellent link performance for different types of modulation schemes even under high data rates and low received optical powers, for instance, achieving effective bit rates of 2.96 and 2.54 Gbps using 16-QAM and 32-QAM modulation schemes, respectively, at a received optical power of -16.4 dBm. We also discuss the implementation aspects of the proposed approach, including its computational complexity.

Original languageEnglish
Title of host publication2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024
PublisherIEEE
Pages534-538
Number of pages5
ISBN (Electronic)979-8-3503-4874-3
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventInternational Symposium on Communication Systems, Networks and Digital Signal Processing - Rome, Italy
Duration: 17 Jul 202419 Jul 2024

Publication series

Name2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024

Conference

ConferenceInternational Symposium on Communication Systems, Networks and Digital Signal Processing
Abbreviated titleCSNDSP
Country/TerritoryItaly
CityRome
Period17/07/202419/07/2024

Keywords

  • KNN classification
  • Machine learning
  • Signal demodulation
  • Underwater wireless optical communications

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