Is DRL-based MAC Ready for Underwater Acoustic Networks? Exploring Its Practicality in Real Field Experiments

2026-05-11

AI Summary

This work addresses the limitations of conventional underwater acoustic network MAC protocols, which suffer from high overhead and difficulty in acquiring accurate environmental and neighbor state information, as well as the gap between existing deep reinforcement learning (DRL)-based approaches—mostly validated only in idealized simulations—and real-world deployment. The study presents EA-MAC, a fully autonomous MAC protocol that robustly handles observation loss while simultaneously optimizing multiple objectives, namely throughput and fairness. For the first time, the feasibility of a DRL-driven MAC protocol is experimentally validated in a real-world underwater acoustic field environment. Deployed on actual acoustic modems, EA-MAC demonstrates superior adaptive scheduling capabilities, significantly outperforming state-of-the-art protocols by achieving concurrent improvements in both throughput and fairness.

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.

Problem

• Underwater Acoustic Networks
• Medium Access Control
• Deep Reinforcement Learning
• Real Field Experiments
• Autonomous Access

Innovation

• Deep Reinforcement Learning
• Underwater Acoustic Networks
• MAC Protocol
• Real Field Experiments
• Autonomous Access