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

arXiv: https://arxiv.org/abs/2503.00669 · HTML: https://mlsanity.com/paper/2503.00669/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Neural Network Detection of Data Sequences in Communication Systems](https://mlsanity.com/paper/1802.02046/) (2018)
- [6G White Paper on Machine Learning in Wireless Communication Networks](https://mlsanity.com/paper/2004.13875/) (2020)
- [Towards Real-Time Advancement of Underwater Visual Quality with GAN](https://mlsanity.com/paper/1712.00736/) (2017)
- [6G Wireless Channel Measurements and Models: Trends and Challenges](https://mlsanity.com/paper/2012.06381/) (2020)

## Built on by (most cited first, 1 of 1)

- [Sex and age determination in European lobsters using AI-Enhanced bioacoustics](https://mlsanity.com/paper/2511.16848/) (2025)

## Abstract (arXiv)

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.