eess.SPSep 14, 2026
SaveMachine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey
Abstract
Underwater Optical Wireless Communication (UOWC) has emerged as a promising technology for high-speed underwater data transmission, offering significantly higher bandwidth and lower latency compared to acoustic and Radio Frequency (RF) technologies. However, the underwater medium introduces severe impairments that degrade link performance and limit communication range. The growing complexity of these challenges has increased research interest in Machine Learning (ML) and Deep Learning (DL) approaches, which offer powerful tools for channel modeling, signal processing, and system adaptation in ways that conventional analytical methods struggle to achieve. This survey provides a comprehensive review of ML methods applied across the full UOWC system pipeline, covering channel modeling, transmitter design, receiver detection and equalization, link alignment, and emerging applications, including wireless power transfer, semantic communication, object detection, optical sensing, and localization. Finally, we discuss open challenges and future research directions to motivate further work in this rapidly growing field.
Figures & tables
| Abbreviation | Definition | Abbreviation | Definition |
|---|---|---|---|
| AE | Autoencoder | MLP | Multilayer Perceptron |
| ANN | Artificial Neural Network | NN | Neural Network |
| APD | Avalanche Photodiode | OAM | Orbital Angular Momentum |
| APT | Acquisition, Pointing, and Tracking | OFDM | Orthogonal Frequency Division Multiplexing |
| AUV | Autonomous Underwater Vehicle | OOK | On-Off Keying |
| BER | Bit Error Rate | OWC | Optical Wireless Communication |
TABLE I: Principal Abbreviations Used in This Survey
Fig. 1: General architecture of an underwater optical wireless communication system. Information is encoded, modulated, and converted into optical signals by an LED /laser transmitter. The optical beam propagates through the UOWC channel, where absorption, scattering, and turbulence degrade the signal, after which photodetection, demodulation, and decoding are performed to recover the transmitted information.
Fig. 2: Organization of the survey. After the fundamentals of UOWC and ML (Section II ), Sections III – VII survey learning-based methods component by component along the UOWC pipeline, followed by open challenges and future research directions (Section VIII ) and the conclusion (Section IX ).
TABLE II: Comparison of Related Surveys
| Pipeline stage | Reference | ML family | Data source | Evaluation |
| Channel modeling & estimation | Zhao et al. [ 28 ] | CNN (AlexNet) | Experimental QAM constellation diagrams | Experiment |
| Al-Amodi et al. [ 29 ] | CNN | Simulated received signal samples | Simulation | |
| Shashikanth et al. [ 30 ] | CNN - LSTM | Bubble turbulence (15 classes) | Experiment | |
| Lu et al. [ 31 ] | CNN + DNN | Harbor, clear, and mixed water channels | Simulation and experiment | |
| Jia et al. [ 32 ] | SRCNN + DnCNN | Experimental OFDM FD- UOWC channels | Experiment | |
| Bo et al. [ 33 ] | CNN - AE hybrid | EGG turbulence model | Simulation |
TABLE III: Classification of Surveyed Machine Learning Approaches According to UOWC System Components
| Pipeline stage | Reference | ML family | Data source | Evaluation |
| Link alignment & adaptive control | Jia et al. [ 67 ] | YOLOv8s + RTMPose-t | ULDB image dataset | Experiment |
| Lu et al. [ 68 ] | CNN | MIMO UOWC | Simulation and experiment | |
| Tanaka et al. [ 69 ] | CNN | Twin-beam images (2.4 m tank) | Experiment | |
| Ishida et al. [ 70 ] | LSTM + DQN | Wave displacement data | Simulation | |
| Romdhane & Kaddoum [ 71 ] | Q-learning + SARSA | 4 water types | Simulation | |
| Shin et al. [ 72 ] | TPTA- DRL (3 agents) | USV trajectory data | Simulation |
TABLE III: Classification of Surveyed Machine Learning Approaches According to UOWC System Components (Continued)
| Parameter | Acoustic | RF | UOWC |
|---|---|---|---|
| Data Rate | kbps | Mbps | Gbps |
| Distance | Up to km | 10 m | 10–100 m |
| Latency | High | Moderate | Low |
| Frequency Band | 10–15 kHz | 30–300 Hz (ELF) / MHz range (buoyant) | – Hz |
| Key Limitation | Low data rate, high latency | Severe seawater attenuation | Limited range, misalignment sensitive |
TABLE IV: Comparison of Underwater Wireless Communication Technologies
Fig. 3: Unified architecture of an ML-enabled UOWC system. Environmental and hardware impairments affect the optical communication pipeline, while learning modules support channel modeling, transceiver optimization, signal recovery, link adaptation, beam control, and AUV positioning.
Fig. 4: CNN architecture operating on digitized received signal samples.
Fig. 5: RL agent-environment interaction loop for UOWC beam adaptation.
Fig. 6: CGAN framework for UOWC channel emulation: the generator synthesizes received signals conditioned on the transmitted input, while the discriminator distinguishes generated from real received signals.
Fig. 7: AE Transceiver Architecture. The transmitter learns a compact signal representation that propagates through the underwater optical channel, while the receiver reconstructs the transmitted information.
Fig. 8: Conceptual framework of semantic-aware transmission in UOWC systems. Semantic features extracted at the transmitter cross the underwater optical channel, which introduces path loss, turbulence, and noise, and are decoded into the reconstructed meaning at the receiver.
| Reference | Model | Dataset | CR | Reported PSNR | Reported SSIM |
| [ 48 ] | ResNet + LSTM | EUVP | 1/16 | 26.92% improvement | 51.90% improvement |
| [ 49 ] | Swin Transformer | EUVP, UIEB, RUIE | – | 2.09 dB gain (BD-PSNR) | 0.0864 gain (BD-SSIM) |
| [ 51 ] | Swin Transformer | EUVP, UIEB, RUIE | 1/6 | 25.462 dB | 0.9417 |
| [ 50 ] | VAE | EUVP, UIEB, LSUI | 1/48 | 22.57 dB | 0.609 |
| [ 52 ] | ConvSC | UFO-120 | 1/16 | 25.8 dB | 8.7 dB MS-SSIM |
| [ 53 ] | RQ - VAE | EUVP | – | 28.89 dB | – |
TABLE V: Performance Comparison of Semantic Communication Systems for Underwater Optical Wireless Communications
Fig. 9: Architecture of the DBN demodulator. Three stacked RBMs perform layer-wise unsupervised feature extraction, followed by a finetuning layer with supervised backpropagation.
| Reference | Method | Signal Format | Main Result | Evaluation |
|---|---|---|---|---|
| [ 59 ] | Shallow DNN | oversampled OOK | Joint equalization and decoding; 2.3 dB and 1.1 dB received-power gains over threshold detection and maximum likelihood estimation. | Experimental |
| [ 62 ] | BGRU | 64- APSK | Maximum Q-factor improvement of 2.4 dB and a transmission rate of 3.19 Gbps. | 1.2-m experimental link |
| [ 64 ] | DeepESN | PAM 4 and QAM - OFDM | Outperformed recursive LS equalization and trained only the output weights using SVD . | 40.5-m experimental link |
| [ 60 ] | TCN-LSTM-AM | PAM and QPSK - OFDM | Achieved 97.99% accuracy; the source reports a BER -performance improvement of up to 71.4% at 130 m relative to its reference results. | Simulation |
| [ 61 ] | CNN | Multiplexed OAM | Directly decoded intensity maps and outperformed the Gerchberg–Saxton algorithm under moderate and strong turbulence. | Numerical simulation |
| [ 66 ] | semiTNN | PAM 4 | Reached 4.26 Gbps using only 5–10% labeled data. | 14-m and 56-m experiments |
TABLE VI: Neural Network Equalizers for UOWC
Fig. 10: Conceptual architecture for joint system-level optimization in ML-enabled UOWC. The cross-layer controller coordinates semantic encoding, adaptive modulation, optical transmission, beam steering, power allocation, and receiver processing across the communication pipeline.
| UOWC Stage | Simulation | Experiment | Simulation and Experiment | Emulated UOWC | Total |
|---|---|---|---|---|---|
| Channel | 4 | 6 | 1 | 0 | 11 |
| Transmitter | 9 | 2 | 1 | 3 | 15 |
| Receiver | 6 | 7 | 1 | 0 | 14 |
| Link alignment | 6 | 3 | 3 | 0 | 12 |
| Emerging applications | 7 | 9 | 0 | 0 | 16 |
| Total | 32 | 27 | 6 | 3 | 68 |
TABLE VII: Validation Modes Reported Across the Surveyed UOWC Pipeline Stages