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Learning assisted mode selection for hybrid optical acoustic underwater wireless communication under stochastic channel conditions

LENIN JOSEPH1, SANGEETHA ANANDAN1,*

Affiliation

  1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

Hybrid optical acoustic underwater wireless communication systems are a promising solution in meeting the challenges offered by complex underwater channels for reliable and efficient data transmission. This paper investigates learning assisted communication mode selection in hybrid optical acoustic underwater wireless networks under stochastic channel conditions, where machine learning classifiers are used to pre assess the link quality, followed by a Bayesian regularization neural network for refined mode selection and improved prediction under uncertain and diverse underwater conditions. Simulation results show better performance in terms of energy consumption, throughput, normalized mean traffic load, received load, and average power savings, compared to existing methods.

Keywords

Underwater wireless communication, Hybrid optical acoustic networks, Mode selection, Bayesian regularization neural networks.

Citation

LENIN JOSEPH, SANGEETHA ANANDAN, Learning assisted mode selection for hybrid optical acoustic underwater wireless communication under stochastic channel conditions, Optoelectronics and Advanced Materials - Rapid Communications, 20, 5-6, May-June 2026, pp.265-276 (2026).

Submitted at: March 17, 2026

Accepted at: June 2, 2026