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dc.contributor.authorThenginthody Hassan, Sabna
dc.contributor.supervisorYue Rongen_US
dc.contributor.supervisorKit Yan Chanen_US
dc.date.accessioned2025-07-31T02:27:55Z
dc.date.available2025-07-31T02:27:55Z
dc.date.issued2025en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/98193
dc.description.abstract

Underwater acoustic (UA) communication is vital for applications like oceanographic data collection and defence but faces challenges like limited bandwidth and multipath interference. This research enhances the conventional OFDM method with a neural network (NN)-based approach, leveraging CNNs, LSTMs, and MLPs for improved channel estimation and adaptability. Experiments in simulations, tank tests, and river trials demonstrated the superiority of the NN-based receiver, particularly under high non-linearity and Doppler effects, advancing UA communication reliability and adaptability.

en_US
dc.publisherCurtin Universityen_US
dc.titleDeep Learning Based High-Speed Underwater Acoustic Communication System Designen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidThenginthody Hassan, Sabna [0000-0003-1788-7448]en_US


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