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Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based

Sabna Thenginthody Hassan1, Peng Chen1, Yue Rong1

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A novel deep neural network (DNN) receiver improves underwater acoustic (UA) communication by learning and compensating for channel non-linearity. This DNN-based approach outperforms traditional methods, offering enhanced reliability and adaptability for future UA systems.

Keywords:
Doppler shiftconvolutional neural networkdeep neural networkleast squareslong short-term memoryorthogonal frequency-division multiplexingunderwater acoustic communication

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Underwater acoustic (UA) communication faces challenges due to multipath propagation causing significant delay spread.
  • Conventional orthogonal frequency-division multiplexing (OFDM) receivers struggle with channel estimation due to non-linear frequency responses between pilot subcarriers in UA channels.
  • The unknown nature of underwater channel delay profiles hinders precise modeling of this non-linearity.

Purpose of the Study:

  • To propose and evaluate a deep neural network (DNN)-based receiver for underwater acoustic (UA) communication.
  • To address the challenge of non-linear channel responses in UA communication by leveraging neural network capabilities.
  • To demonstrate the performance improvement of a DNN-based receiver over conventional methods in real-world UA environments.

Main Methods:

  • Development of a deep neural network (DNN) architecture for underwater acoustic (UA) communication receiver.
  • Training the neural network (NN) to learn and compensate for channel non-linearity inherent in UA communication.
  • Comparative performance analysis against conventional least-squares (LS) estimator-based receivers using data from river trials.

Main Results:

  • The DNN-based UA communication receiver demonstrated superior performance compared to the conventional least-squares (LS) estimator.
  • River trials in Western Australia validated the effectiveness of the proposed DNN-based receiver.
  • The results indicate successful learning and compensation of non-linear channel effects by the neural network.

Conclusions:

  • Deep neural network (DNN) receivers offer a promising solution for revolutionizing underwater acoustic (UA) communication.
  • The proposed DNN-based approach enables higher data rates, improved reliability, and better adaptability to dynamic underwater conditions.
  • Future UA communication systems can benefit significantly from the integration of DNN receiver technology.