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QoS-aware joint access control and duty cycle control for machine-to-machine communications

机译:用于机器对机器通信的Qos感知联合访问控制和占空比控制

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摘要

Massive devices and various applications imposes new challenges for Machine-to-Machine (M2M) communications to enable Internet of Things (IoT). In this paper, we investigate a QoS-aware joint access control and duty cycle control problem for M2M communications to optimise the overall network performance, including energy efficiency, end-to-end delay, reliability, throughput and fairness. We first model a practical hybrid M2M communication network and measure the overall network performance through a cost function. Then, an optimisation problem is formulated to minimise the long-term aggregated network cost. Further more, we overcome the non-convexity of the cost function and mathematically derive the optimal access control. Finally, we propose a distributed access control followed by a reinforcement learning (RL) based duty cycle control which adapts to various network dynamics without priori network information. Simulation results show that, the proposed joint access control and duty cycle control minimise the network long-term aggregated cost, while achieving fairness among cluster heads with QoS differentiation.
机译:大型设备和各种应用程序对机器对机器(M2M)通信以实现物联网(IoT)提出了新的挑战。在本文中,我们研究了用于M2M通信的QoS感知联合访问控制和占空比控制问题,以优化整体网络性能,包括能效,端到端延迟,可靠性,吞吐量和公平性。我们首先对实用的混合M2M通信网络进行建模,然后通过成本函数衡量整体网络性能。然后,提出了一个优化问题,以最大程度地减少长期累积的网络成本。此外,我们克服了成本函数的非凸性,并从数学上推导了最佳访问控制。最后,我们提出了一种分布式访问控制,其后是基于增强学习(RL)的占空比控制,该占空比控制可适应各种网络动态情况而无需先验网络信息。仿真结果表明,所提出的联合访问控制和占空比控制可以最大程度地降低网络的长期总成本,同时通过QoS区分实现簇头之间的公平性。

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