Exploring the vast underwater realm, from monitoring marine ecosystems to managing subsea infrastructure and conducting defense operations, demands robust and high-speed communication. Traditional methods like acoustic waves offer long range but suffer from low bandwidth, while radio frequencies struggle to penetrate water effectively ([8],,,). This creates a significant data bottleneck, hindering real-time operations and the transmission of large datasets. However, the convergence of light-based communication and artificial intelligence (AI) is forging a path to shatter this aquatic data barrier.
Optical wireless communication (OWC), often referred to as Underwater Wireless Optical Communication (UWOC) or Underwater Visible Light Communication (UVLC), emerges as a transformative alternative ([9],,). Using light, particularly in the blue-green part of the spectrum where water exhibits the least absorption, offers the potential for significantly higher bandwidth – orders of magnitude greater than acoustic or RF methods ([9],,). Technologies like Light Fidelity (Li-Fi) leverage LEDs for data transmission, promising data rates reaching Gbps levels, enabling applications previously unimaginable, such as real-time video streaming and high-performance sensor networks ([1],,,). Recent experiments have demonstrated impressive data rates, exceeding 10 Gbps in some cases and even pushing towards 30 Gbps over shorter distances ([1],).
Despite its promise, harnessing light underwater isn't without hurdles. The aquatic medium itself presents formidable challenges ([2],). Absorption limits the signal's strength, while scattering caused by water molecules and suspended particles deviates photons, weakening the signal and limiting range ([15],,). Furthermore, oceanic turbulence can cause rapid fluctuations in the received signal strength (scintillation) and make maintaining precise alignment between transmitters and receivers difficult ([7],,). Environmental factors like turbidity, salinity, and temperature gradients further complicate light propagation ([7],).
This is where Artificial Intelligence (AI) and Machine Learning (ML) step in as crucial enablers ([1],). AI algorithms are proving instrumental in overcoming the inherent unpredictability and harshness of the underwater channel.
- Intelligent Channel Prediction and Adaptation: AI can analyze and predict underwater channel conditions in real-time. This allows systems to dynamically optimize communication parameters like modulation schemes (e.g., adapting OFDM techniques) and coding strategies to maintain reliable connections even in fluctuating environments ([1],). Smart transceivers can even use AI to adjust physical parameters, like the divergence angle of the light beam, based on the received signal strength, improving link robustness ([7]).
- Enhanced Signal Processing and Data Recovery: ML techniques, particularly deep learning models like Convolutional Neural Networks (CNNs), are being employed to recover transmitted data from weak or distorted optical signals ([11],). These AI models can effectively filter noise and compensate for channel impairments, often without needing prior detailed knowledge of the channel characteristics, significantly improving the bit-error rate (BER) performance ([11],). AI is also used to enhance the quality of underwater images and video captured and transmitted ([3]).
- Smart Network Management: In complex underwater networks, like the envisioned Internet of Underwater Things (IoUT), AI plays a vital role in managing resources efficiently ([1],,,). AI algorithms can handle dynamic network routing, allocate bandwidth effectively, and manage interference ([1]). Recent breakthroughs include frameworks for Automatic Network Slicing (ANS), which uses AI to allocate communication resources based on real-time network conditions and application requirements ([1],).
- Facilitating Hybrid Systems: Recognizing that no single technology is perfect, AI facilitates the operation of hybrid communication systems. These systems might combine the long-range capabilities of acoustics with the high-speed data transfer of optics ([1],). AI can intelligently manage switching between these modes based on environmental conditions and task requirements, balancing connectivity and data throughput ([1]). Innovations like the Universal Underwater Software Defined Modem (UniSDM) embody this flexibility, capable of using sound, light, magnetic induction, and radio waves, and potentially using AI to optimize their combined use ([1],).
The integration of light-based communication and AI is rapidly advancing. Researchers are developing novel solutions like non-line-of-sight (NLOS) communication techniques leveraging light scattering and innovative detectors like scintillating fibers or large-area photovoltaic cells to ease stringent alignment requirements and potentially harvest energy ([9]).
By combining the high-bandwidth potential of optical waves with the adaptive intelligence of AI, we are overcoming the limitations of the underwater environment. This synergy is paving the way for reliable, high-speed underwater networks, unlocking unprecedented capabilities for scientific research, environmental monitoring, resource exploration, defense, and the seamless operation of autonomous underwater systems in the vast, data-rich aquatic domain ([1],,,). The future of underwater exploration and exploitation hinges on this bright and intelligent approach to communication.