A state-of-the-art survey and benchmarking of Adaptive Modulation and Coding for Underwater Acoustic Communications
Year: 2026
Type: article
Abstract: Adaptive Modulation and Coding (AMC) is a critical technique for optimising data transmission in the highly variable and challenging domain of Underwater Acoustic Communication (UWAC). This survey reviews AMC methodologies across three paradigms: traditional (non-ML), supervised machine learning (ML), and reinforcement learning (RL). The evolution is traced from simple rule-based heuristics to data-driven polices that address noise, long propagation delays, multi-path, Doppler, and outdated channel state information (CSI). We then implement four representative classes of approach—SNR thresholding baselines, boosted tree classifiers, model-free multi-armed bandits and a model-based LSTM DQN. These approaches are comprehensively evaluated on an open-source experimental dataset. While traditional and supervised methods perform strongly in stable regimes, model-based deep RL shows superior adaptability, achieving the best throughput-reliability balance in time-varying channels. Finally, we discuss key open research challenges including data scarcity and model generalisation and highlight future directions for developing more robust, intelligent, and efficient AMC systems for UWAC. • Survey AMC for UWAC across traditional, ML/DL, and RL paradigms; provide a clear taxonomy and trade-offs. • Introduce a reproducible ACommSet benchmark spanning SNR-thresholding, boosted trees, MAB, and LSTM-DQN. • Show the value of temporal information: LSTM-based models exploit sequential CSI and yield the best through put-BER trade-off.
Source: Applied Ocean Research
Institution: Flinders University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,950
OpenAlex ID: W4414365689