An Asynchronous Triggered MAC Protocol for Underwater Acoustic Networks

2026-08-11

AI Summary

This work addresses the limitations of conventional underwater acoustic network MAC protocols, which rely on synchronized time slots and struggle with variable propagation delays, leading to rigid scheduling and low channel utilization. To overcome these challenges, the paper proposes an Asynchronous Triggered MAC protocol (AT-MAC) that eliminates the need for time synchronization by employing asynchronous, variable-length slots. AT-MAC integrates an enhanced multi-agent deep reinforcement learning framework to enable efficient coordination among nodes using only local observations. Furthermore, it infers global fairness through local channel monitoring and dynamically adapts its scheduling policy accordingly. Extensive simulations demonstrate that AT-MAC significantly outperforms existing protocols across diverse scenarios and traffic loads, achieving higher channel utilization and improved network adaptability.

Abstract

Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have proven their practicality through extensive field trials in Underwater Acoustic Networks (UANs), attributable to their hardware-agnostic and easily implementable properties. Most existing protocols rely on a synchronized and fixed-length slot paradigm to mitigate channel contention and facilitate orderly transmissions. However, this paradigm imposes significant clock synchronization overhead in UANs with low and variable sound speed and struggles to improve scheduling flexibility. Although some protocols attempt to refine this slot paradigm (adjust the slot length to improve channel reuse efficiency or scheduling frequency), they are still constrained by the trade-off between channel utilization and scheduling complexity. To this end, this paper advocates a paradigm shift in underwater MAC design, transitioning from synchronized slot to asynchronous scheduling, and realizes it through an Asynchronous Triggered MAC (AT-MAC). AT-MAC introduces a triggered slot paradigm without time synchroniza?tion, decoupling transmission scheduling from a rigid timeline and enabling asynchronous, variable-length slots to accommodate the long and diverse propagation delays. To power this slot paradigm, AT-MAC augments conventional Multi-Agent Deep Reinforcement Learning to handle asynchronous interaction, achieving efficient coordinated channel access under partial ob?servations. It further devises a load-aware fairness guard mech?anism to enable network-wide fairness status inference solely through local overhearing, thereby guiding adaptive scheduling correction to maintain fairness. Trace-based and on-board ex?periments validate the feasibility and computational practicality of AT-MAC. Extensive simulation results further demonstrate its superiority and adaptability across various scenarios and traffic conditions.

Problem

• Underwater Acoustic Networks
• Medium Access Control
• Time Synchronization
• Channel Contention
• Scheduling Flexibility

Innovation

• Asynchronous MAC
• Underwater Acoustic Networks
• Triggered Slot
• Multi-Agent Deep Reinforcement Learning
• Fairness-aware Scheduling