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
高煜 1肖俏 1王超峰1
作者信息
- 1. 南华大学计算机学院,湖南 衡阳,421001
- 折叠
摘要
水声通信因其高传播时延、信道时变特性及带宽受限等因素,在传输调度决策方面面临诸多挑战.为提升复杂水声环境下的通信效率,文中提出了一种基于多智能体强化学习(MARL)的水声网络跨层传输调度(TS)方法MARL-TS.该方法针对高水声传播时延和动态信道环境,以传输节点的数据缓存状态与信道条件为基础,以通信网络的传输效率和传输时延为优化目标,自适应地进行跨层优化,实现功率分配与时隙资源调度的联合优化.为学习最优传输策略,文中构建了可学习的策略网络与价值网络,并结合多智能体协同学习,提升策略优化的效率与自适应决策能力.仿真实验表明,与现有基于强化学习的多路访问控制协议相比,MARL-TS在传输能效优化和传输时延降低等方面表现出显著优势,尤其在多节点高负载场景下展现了更强的适应性与稳定性,为复杂水下通信系统的优化提供了新思路.
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引用本文复制引用
出版年
2025
NETL
NSTL
万方数据