物联网中支持认知无线电manet的最优QoS多播路由协议:深度q -学习方法

T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An
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引用次数: 4

摘要

在本文中,我们提出了一种基于深度q学习方法的物联网(IoT)中实现认知无线电移动自组织网络(icr - manet)的最佳服务质量(QoS)组播路由协议(QMR)。为此,我们提出了一个联合端到端排队延迟和链路稳定性优化问题。公式优化通常是np完全的。然后,我们利用一种新颖的深度Q-学习方法来解决这个具有挑战性的问题,该方法达到最优收敛$Q^{\ast}$-值。基于得到的$Q^{\ast}$值,提出了一种QoS组播路由协议,选择$Q^{\ast}$值最小的最佳邻居集,建立到组播成员的组播树。仿真结果表明,所提出的QMR协议优于当前最先进的路由协议,是icr - manet中一种高效的组播路由协议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimal QoS Multicast Routing Protocol in IoT Enabling Cognitive Radio MANETs: A Deep Q-Learning Approach
In this paper, we propose an optimal quality-of-service (QoS) multicast routing protocol (QMR) in Internet-of-Things (IoT) enabling cognitive radio mobile ad hoc networks (ICR-MANETs) based on deep Q-learning approach. To this end, we formulate a joint end-to-end queuing delay and link’s stability optimization problem. The formulated optimization is typically NP-complete. We then leverage a novel deep Q-learning method to solve this challenging problem, which arrives at optimal convergence $Q^{\ast}$-values. Based on the obtained $Q^{\ast}$-values, we propose a QoS multicast routing protocol to select the best set of neighbors associated with minimum $Q^{\ast}$-values to establish the multicast tree to the multicast members. Simulation results show that the proposed QMR protocol outperforms the current state-of-the-art routing protocols, which emerges as an efficient multicast routing protocol in ICR-MANETs.
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