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Wireless Link Selection Methods for Maritime Communication Access Networks-A Deep Learning Approach.

Michal Hoeft¹, Krzysztof Gierlowski¹, Jozef Wozniak¹

¹Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, G. Narutowicza 11/12, 80-233 Gdansk, Poland.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces deep neural networks for maritime link selection, improving vessel communication reliability. The new method enhances prediction accuracy and reduces testing traffic in heterogeneous on-shore wireless networks.

Keywords:

deep learning maritime communication wireless heterogeneous networks

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Area of Science:

Maritime communication systems
Wireless networking technologies
Machine learning applications

Background:

Growing demand for reliable data transmission in coastal waters.
Challenges posed by heterogeneous on-shore wireless infrastructures managed by multiple operators.
Need for robust network mechanisms to ensure continuous communication for sea vessels.

Purpose of the Study:

To explore the application of deep neural networks for link selection in maritime communication.
To address the critical challenge of seamless network transitions for mobile sea vessels.
To improve the efficiency and accuracy of link selection processes in complex maritime environments.

Main Methods:

Overview of maritime communication requirements and characteristics.
Development and application of deep neural network models for link selection.
Verification using realistic maritime communication propagation models.
Comparative analysis against existing popular solutions.

Main Results:

Deep neural networks demonstrate significant gains in prediction accuracy for link selection.
The proposed methods lead to a substantial reduction in the required test traffic for measurements.
Improved reliability and efficiency in maintaining communication for sea vessels.

Conclusions:

Deep neural networks offer a promising solution for effective link selection in maritime communication.
The developed approach enhances the performance of heterogeneous wireless access infrastructure for vessels.
This research contributes to more reliable and efficient maritime data transmission.