水下无人系统学报2025，Vol.33Issue(2)：261-271,11.DOI:10.11993/j.issn.2096-3920.2025-0015
时变水声信道下基于多智能体强化学习的水声网络跨层传输调度方法
MARL-TS Method for Underwater Acoustic Networks in Time-Varying Channels
摘要
Abstract
Underwater acoustic communication faces numerous challenges in transmission scheduling and decision-making due to its high propagation delay,time-varying channel characteristics,and limited bandwidth.To enhance communication efficiency in complex underwater acoustic environments,this paper proposed a multi-agent reinforcement learning(MARL)-based cross-layer transmission scheduling(TS)method for underwater acoustic networks,termed MARL-TS.This method addressed the high propagation delay and dynamic channel environments by leveraging transmission node buffer states and channel conditions as the foundation while optimizing transmission efficiency and transmission delay of the communication network.It adaptively performs cross-layer optimization to jointly optimize power allocation and timeslot resource scheduling.To learn the optimal transmission strategy,this paper constructed a learnable policy network and a value network,integrating multi-agent cooperative learning to improve strategy optimization efficiency and adaptive decision-making capabilities.Simulation results demonstrate that compared with existing reinforcement learning-based multiple access control(MAC)protocols,MARL-TS significantly enhances transmission efficiency and reduces transmission delay.Notably,it exhibits superior adaptability and stability in multi-node and high-load scenarios,offering a novel approach for optimizing complex underwater communication systems.关键词
水声通信网络/时变信道/多智能体强化学习/跨层传输Key words
underwater acoustic network/time-varying channel/multi-agent reinforcement learning/cross-layer transmission scheduling分类
武器工业引用本文复制引用
高煜,肖俏,王超峰..时变水声信道下基于多智能体强化学习的水声网络跨层传输调度方法[J].水下无人系统学报,2025,33(2):261-271,11.基金项目
国家自然科学基金项目(62201248) （62201248）
湖南省自然科学基金项目(2023JJ40556). （2023JJ40556）
