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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Statistical QoS provisioning for MTC networks under finite blocklength
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Statistical QoS provisioning for MTC networks under finite blocklength

机译:有限块长度下MTC网络的统计QoS配置

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This paper analyzes the effective capacity of delay-constrained machine-type communication (MTC) networks operating in the finite blocklength regime. First, we derive a closed-form mathematical approximation for the effective capacity in quasi-static Rayleigh fading channels. We characterize the optimum error probability to maximize the concave effective capacity function with reliability constraint and study the effect of signal-to-interference-plus-noise ratio (SINR) variations for different delay constraints. The trade-off between reliability and effective capacity maximization reveals that we can achieve higher reliability with limited sacrifice in effective capacity specially when the number of machines is small. Our analysis reveals that SINR variations have less impact on effective capacity for strict delay-constrained networks. We present an exemplary scenario for massive MTC access to analyze the interference effect proposing three methods to restore the effective capacity for a certain node which are power control, graceful degradation of delay constraint, and joint compensation. Joint compensation combines both power control and graceful degradation of delay constraint, where we perform the maximization of an objective function whose parameters are determined according to the delay and SINR priorities. Our results show that networks with stringent delay constraints favor power controlled compensation, and compensation is generally performed at higher costs for shorter packets.
机译:本文分析了在有限块长度方案中运行的延迟约束机型通信(MTC)网络的有效容量。首先,我们从准静态瑞利褪色通道中获得有效容量的封闭式数学近似。我们表征了最佳误差概率,以使凹入有效的容量功能最大化,可靠性约束,研究了不同延迟约束的信号对干扰噪声比(SINR)变化的影响。可靠性和有效能力最大化之间的权衡揭示了我们可以在机器数量小的情况下特别能够在有效的能力中获得更高的可靠性。我们的分析表明,SINR变化对严格延迟约束网络的有效能力影响较小。我们介绍了大规模MTC访问的示例性情景,以分析提出三种方法以恢复为特定节点的有效容量的干扰效果,这是延迟约束的正常劣化和关节补偿。关节补偿结合了延迟约束的功率控制和优常劣化,在那里我们执行根据延迟和SINR优先级确定的目标函数的最大化。我们的结果表明,具有严格延迟约束的网络有利于功率控制补偿,并且通常以更高的数据包以更高的成本执行补偿。

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