Is DRL-based MAC Ready for Underwater Acoustic Networks? Exploring Its Practicality in Real Field Experiments
2026-05-11 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors studied how to use Deep Reinforcement Learning (DRL) to improve underwater communication networks, which face problems like slow signal travel and changing water conditions. They found that existing methods did not fully solve these problems, often relying on unrealistic tests or only partial solutions. To fix this, they created a new DRL-based protocol called EA-MAC that handles lost observations and balances different goals to let underwater devices communicate efficiently on their own. They tested EA-MAC in real underwater experiments and showed it helps devices share the communication channel fairly and quickly.
Medium Access ControlUnderwater Acoustic NetworksDeep Reinforcement LearningPropagation DelayObservation LossMAC ProtocolsUnderwater Acoustic ChannelSchedulingAutonomous AccessCommunication Throughput
Authors
Jiani Guo, Bingwen Huangfu, Shanshan Song, Nan Sun, Miao Pan, Guangjie Han
Abstract
Medium Access Control (MAC) protocols rely on neighbor and environment information to design collision-free access rules for Underwater Acoustic Networks (UANs). Acquiring this information suffers from high communication overhead due to the unique underwater acoustic channel characteristics, such as long propagation delay, spatiotemporal variations in communication quality, and high attenuation. Deep Reinforcement Learning (DRL) is promising to circumvent the UANs' physical constraints and provide a low-overhead solution for underwater MAC protocols, since it can decide access rules based on real-time observation without extra information exchange. However, the unique underwater acoustic channel characteristics impose significant challenges on observation acquisition, training time, and the balance of multiple reward factors for DRL-based MAC protocols. Most existing methods remain at the theoretical level: (1) they design partial intelligent agents failing to achieve fully autonomous access; (2) they assume unreasonable simulation scenarios, weakening the effects of underwater acoustic channel characteristics on MAC protocols. To enhance the practicality of DRL-based MAC protocols, we first analyze the application challenges of DRL in UANs through real field experiments. Based on the above challenges, we propose a DRL-based MAC protocol that considers observation loss and balances multiple reward factors to achieve efficient Entire Autonomous access in the UAN (EA-MAC). To further explore the feasibility of DRL-based MAC protocols, we implement EA-MAC and other state-of-the-art protocols on underwater acoustic modems and evaluate their performance in real field experiments. Experimental results demonstrate that EA-MAC can adaptively determine the scheduling sequence for each node, enabling high-throughput and fair communication in a straightforward manner for UANs.