A Survey of Deep/Machine Learning in Maritime Communications

In recent years Machine learning has begun to show its potential in all domains of life, including the field of maritime communications. Research on and attempts to use Machine learning in maritime communications have been conducted in recent years. Some of the major research areas of Machine Learning driven maritime communications are channel selection, channel coding, synchronization, and positioning system. To this purpose, the incorporation of ML into maritime communications adds a dimension to wireless connectivity that surpasses current deployments, which mostly rely on satellite links with significant latency and shore-based base stations with limited coverage.

[1]  J. Wozniak,et al.  Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach , 2022, Sensors.

[2]  P. Bithas,et al.  A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges , 2022, IEEE Open Journal of the Communications Society.

[3]  Wei Yang Bryan Lim,et al.  Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things , 2022, IEEE Transactions on Network Science and Engineering.

[4]  Wided Hammedi,et al.  Federated Deep Learning-Based Framework to Avoid Collisions Between Inland Ships , 2022, 2022 International Wireless Communications and Mobile Computing (IWCMC).

[5]  Naser Hossein Motlagh,et al.  Deep Learning and the Oceans , 2022, Computer.

[6]  Shehzad Ashraf Chaudhry,et al.  A resource friendly authentication scheme for space–air–ground–sea integrated Maritime Communication Network , 2022, Ocean Engineering.

[7]  Liang Xiao,et al.  UAV-Aided Anti-Jamming Maritime Communications: A Deep Reinforcement Learning Approach , 2021, 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP).

[8]  Zhonghai Wang,et al.  An Overview on Position Location: Past, Present, Future , 2021, Int. J. Wirel. Inf. Networks.

[9]  Petteri Nurmi,et al.  Toward Large-Scale Autonomous Monitoring and Sensing of Underwater Pollutants , 2020, ArXiv.

[10]  Sheng Wu,et al.  Deep reinforcement learning based joint edge resource management in maritime network , 2020, China Communications.

[11]  Gangbing Song,et al.  Inspection and monitoring systems subsea pipelines: A review paper , 2020, Structural Health Monitoring.

[12]  Alfonso Gómez-Espinosa,et al.  Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions , 2020, Applied Sciences.

[13]  Nan Cheng,et al.  A Novel Transmission Scheduling Based on Deep Reinforcement Learning in Software-Defined Maritime Communication Networks , 2019, IEEE Transactions on Cognitive Communications and Networking.

[14]  Chiara Petrioli,et al.  CARMA: Channel-Aware Reinforcement Learning-Based Multi-Path Adaptive Routing for Underwater Wireless Sensor Networks , 2019, IEEE Journal on Selected Areas in Communications.

[15]  Alamgir M S M,et al.  Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach , 2020, IEEE Access.