用于水下声网络的异步触发MAC协议
An Asynchronous Triggered MAC Protocol for Underwater Acoustic Networks
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中文总结 AI 辅助
针对水下声网络MAC协议同步开销大、调度灵活性不足的问题，提出异步触发MAC（AT-MAC），采用异步可变时隙与增强多智能体深度强化学习，结合负载感知公平性机制，提升了信道利用率与公平性。
中文摘要 AI 辅助
基于时分多址（TDMA）的介质访问控制（MAC）协议凭借与硬件无关且易于实现的特性，已通过水下声网络（UAN）中的大量现场试验验证了其实用性。现有多数协议依赖同步且固定长度的时隙范式来减轻信道竞争并促进有序传输，但该范式在声速低且可变的UAN中会产生显著的时钟同步开销，且难以提升调度灵活性。尽管部分协议尝试优化该时隙范式（如调整时隙长度以提升信道复用效率或调度频率），但仍受限于信道利用率与调度复杂度之间的权衡。为此，本文主张水下MAC设计的范式转变，从同步时隙转向异步调度，并通过异步触发MAC（AT-MAC）实现这一转变。AT-MAC引入无需时间同步的触发时隙范式，将传输调度与刚性时间线解耦，采用异步可变长度时隙以适配长且多样的传播延迟。为支撑该时隙范式，AT-MAC对传统多智能体深度强化学习进行增强，以处理异步交互，在部分可观测条件下实现高效的协调信道访问；还设计了负载感知公平性保护机制，仅通过本地监听即可实现全网公平性状态推断，进而指导自适应调度修正以维持公平性。基于轨迹的实验与实装实验验证了AT-MAC的可行性与计算实用性，大量仿真结果进一步证明其在各类场景与流量条件下的优越性与适应性。
英文摘要
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 compatibility and ease of implementation. In conventional TDMA-based MAC designs, channel access is typically organized using synchronized, fixed-length slots to mitigate contention and coordinate transmissions. However, this paradigm imposes significant clock synchronization overhead in UANs with long and variable propagation delays 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 proposes AT-MAC, an Asynchronous Triggered MAC protocol that aims to achieve efficient and fair channel access through coordinated asynchronous scheduling. AT-MAC introduces a triggered slot paradigm without time synchronization, 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 coordinated channel access under partial observations. It further devises a load-aware fairness guard mechanism to enable network-wide fairness status inference solely through local overhearing, thereby guiding adaptive scheduling correction to maintain fairness. Field-reconstructed simulations and on-board inference benchmarking demonstrate the feasibility of AT-MAC. Extensive simulation results further demonstrate its consistent performance gains across the evaluated scenarios and traffic conditions.
