Self-Adaptive Resource Allocation in Underwater Acoustic Interference Channel: A Reinforcement Learning Approach
Self-Adaptive Resource Allocation in Underwater Acoustic Interference Channel: A Reinforcement Learning Approach
复制标题
水下声干扰通道的自适应资源分配：一种强化学习方法
DOI：
10.1109/jiot.2019.2962915
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发表时间：
2020-04-01
影响因子：
10.6
通讯作者：
Qian, Jiangbo
中科院分区：
文献类型：
--
作者：
Wang, Hui;Li, Youming;Qian, Jiangbo
由于水声信道是由多个异构实体共享的，并且会受到严重的干扰，水声通信网络（UACNs）面临着通过实现分布式资源分配来减轻干扰和提高通信质量的挑战。在这篇文章中，我们引入了智能控制中的强化学习的概念，将节点视为智能代理，将节点网络视为多代理网络。通过对状态空间和动作空间的划分，给出了一种奖励函数和搜索策略，并提出了一种基于协作<inline-formula><tex-math notation="LaTeX">Q$</tex-math></inline-formula>-学习的分布式资源分配算法。此外，我们还验证了算法的收敛性。最后，在两种不同的水下应用场景下的仿真结果表明，所提算法在提高网络传输容量方面优于现有算法，并且通过采用协同<inline-formula><tex-math notation="LaTeX">Q$</tex-math></inline-formula>-Learning可以减少资源分配的开销。
Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this article, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multiagent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-Learning.