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Employing feedforward backpropagated neural network for Doppler scale estimation in underwater acoustic CP-OFDM communication

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Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 43734-43749
Autor principal: Muzzammil, Muhammad
Otros Autores: Saleem, Shahzad, Ahmed, Niaz, Qiao, Gang, Alharbi, Ayman
Publicado:
Nature Publishing Group
Materias:
Doppler effect
Communication channels
Deep learning
Algorithms
Acoustics
Signal processing
Neural networks
Methods
Communications systems
Underwater
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Orthogonal frequency division multiplexing (OFDM) is a promising solution for underwater acoustic communication (UWA); however, it requires careful handling of the challenges of large multipath and severe Doppler effects inherent in underwater acoustic communication. This paper proposes a novel feedforward backpropagated neural network (FBNN) implementation for Doppler scaling estimation using UWA cyclic-prefix (CP) OFDM communication. A two-layered input-output feedforward network is utilized with three different backpropagated training algorithm variants: Fletcher-Reeves Conjugate Gradient (CGF), Polak-Ribiére Conjugate Gradient (CGP), and Conjugate Gradient with Powell/Beale Restarts (CGB). The proposed approach calculates the Doppler scale factor by combining the neural computational power with the accuracies offered by the three training algorithms. To evaluate the effectiveness of the proposed FBNN implementation, root mean square error (RMSE) is used as a performance metric for different multipath and signal-to-noise ratio (SNR) channel conditions. The paper also presents a comparison of the proposed FBNN-based training algorithms’ performance with that of the benchmark offered by conventional methods.
ISSN:2045-2322
DOI:10.1038/s41598-025-27808-x
Fuente:Science Database