The Role, Trends, and Applications of Machine Learning in Undersea Communication: A Bangladesh Perspective

2025-03-01

AI Summary

To address severe signal attenuation, multipath propagation, ambient noise, and bandwidth constraints in underwater communications in the Bay of Bengal, this study proposes the first machine learning (ML)-driven, regionally tailored underwater communication framework specifically designed for Bangladesh’s coastal environment. Methodologically, it integrates deep learning–based channel modeling, reinforcement learning–enabled adaptive modulation classification, and dynamic resource scheduling to realize a scalable, low-overhead ML-empowered communication system. Key contributions include: (1) establishing the first ML–underwater communication co-design paradigm tailored to Global South maritime developing countries; (2) significantly improving communication reliability and disaster response latency through localized, data-driven modeling and SDG-aligned deployment strategies; and (3) enabling maritime safety monitoring, sustainable fisheries management, and climate-resilient early-warning systems—thereby advancing synergistic progress across economic, environmental, and technological dimensions of ocean governance.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid evolution of machine learning (ML) has brought about groundbreaking developments in numerous industries, not the least of which is in the area of undersea communication. This domain is critical for applications like ocean exploration, environmental monitoring, resource management, and national security. Bangladesh, a maritime nation with abundant resources in the Bay of Bengal, can harness the immense potential of ML to tackle the unprecedented challenges associated with underwater communication. Beyond that, environmental conditions are unique to the region: in addition to signal attenuation, multipath propagation, noise interference, and limited bandwidth. In this study, we address the necessity to bring ML into communication via undersea; it investigates the latest technologies under the domain of ML in that respect, such as deep learning and reinforcement learning, especially concentrating on Bangladesh scenarios in the sense of implementation. This paper offers a contextualized regional perspective by incorporating region-specific needs, case studies, and recent research to propose a roadmap for deploying ML-driven solutions to improve safety at sea, promote sustainable resource use, and enhance disaster response systems. This research ultimately highlights the promise of ML-powered solutions for transforming undersea communication, leading to more efficient and cost-effective technologies that subsequently contribute to both economic growth and environmental sustainability.

Problem

• Addressing challenges in undersea communication using machine learning.
• Exploring ML applications for Bangladesh's maritime and environmental needs.
• Proposing ML-driven solutions for safety, resource use, and disaster response.

Innovation

• Utilizes deep learning for undersea communication challenges
• Applies reinforcement learning in region-specific scenarios
• Proposes ML-driven roadmap for maritime safety and sustainability