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Stochastic game-based dynamic information delivery system for wireless cooperative networks

机译:无线合作网络中基于随机游戏的动态信息传递系统

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The haptic communications is considered as the prime application running on the Tactile Internet. Therefore, Tactile Internet required to be highly reliable, provide a very low latencies, and required sufficient capacities at intermediate nodes to allow a large number of devices to communicate with each other simultaneously and autonomously. Moreover, the wireless cooperative network (WCN), is considered as one of the major component of the 5G technologies due to it promising advantages, such as improving wireless transmission capacity and reliability. However, the selfish nature of relay nodes may depress such enhancement and is not favored by the source node. In this paper, we propose an incentive-based dynamic flow allocation (FA) and forwarding strategy selection (FSS) scheme under time-varying selfishness. In the proposed scheme, the source node determines the FA to maximize the average network throughput under the constraints of network stability and selfishness boundaries, while each selfish relay executes the FSS to optimize its own profit with regard to the dynamic network state. Moreover, to cope with the conflicting interests between selfish relays a stochastic game model is employed to design a competition for haptic information forwarding and Nash equilibrium is proven also a combined Q-learning-based algorithm is proposed to guide the relays' forwarding strategies. Furthermore, by considering the stochastic property of the network state, the FA for the source is formulated as a stochastic optimization problem. Finally, by exploiting the concept of virtual selfishness queue, the problem is solved by using the Lyapunov optimization theory. Performance of the proposed scheme is evaluated with traditional FA approach and data queue-based FA approach. Numerical results exhibit that our scheme not only sustains a large network throughput but also achieves low latency and avoids the occurrence of a completely selfish relay in the long term. (C) 2019 Elsevier B.V. All rights reserved.
机译:触觉通信被认为是在触觉互联网上运行的主要应用程序。因此,触觉互联网需要高度可靠,提供非常低的延迟,并且在中间节点处需要足够的容量以允许大量设备同时自动地相互通信。此外,无线协作网络(WCN)由于具有前途的优势(例如,提高无线传输容量和可靠性)而被视为5G技术的主要组成部分之一。但是,中继节点的自私性质可能会抑制这种增强,并且源节点不希望这样做。在本文中,我们提出了一种基于时变自私的基于激励的动态流分配(FA)和转发策略选择(FSS)方案。在提出的方案中,源节点确定FA以在网络稳定性和自私性边界的约束下最大化平均网络吞吐量,而每个自私中继都执行FSS以优化其自身在动态网络状态方面的利润。此外,为解决自私中继之间的利益冲突,采用随机博弈模型设计了一种面向触觉信息转发的竞赛,证明了纳什均衡,并提出了一种基于Q学习的组合算法来指导中继的转发策略。此外,通过考虑网络状态的随机性,将源的FA公式化为随机优化问题。最后,通过利用虚拟自私队列的概念,利用李雅普诺夫优化理论解决了这一问题。通过传统的FA方法和基于数据队列的FA方法来评估所提出方案的性能。数值结果表明,我们的方案不仅维持较大的网络吞吐量,而且实现了低延迟,并且从长远来看避免了完全自私的中继的发生。 (C)2019 Elsevier B.V.保留所有权利。

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