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Cognitive-based resource allocation scheme in LTE-A networks with M2M/H2H coexistence

机译:M2M / H2H共存的LTE-A网络中基于认知的资源分配方案

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An efficient radio access strategies are necessary and critical to manage an LTE network system where machine-to-machine (M2M) devices and human-to-human (H2H) users coexist. In this paper, we propose a cognitive-based access strategy with a priority-based queuing model that is designed for LTE with M2M/H2H coexistence, where the M2M communications have real time (M2M-RT) and non-real time (M2M-NRT) traffic. Radio access gives the highest priority to H2H, while M2M-RT has higher priority than M2M-NRT. A continuous-time Markov chain model is developed to evaluate the system performance in terms of service completion rate, blocking and forced termination probabilities and mean queuing delay of the M2M traffic. In addition, resource utilization by the M2M traffic is also evaluated. Analytical results reveal that, while protecting the H2H services, the proposed queuing model could increase the capacity of the M2M traffic network while decreasing the blocking probability. Additionally, allowing an interrupted M2M-NRT to be inserted back into its queue can further decrease the forced termination rate. For these reasons, it can be concluded that the proposed model can be used to improve the system performance of multi-channel M2M communication networks.
机译:有效的无线电接入策略对于管理机器对机器(M2M)设备和人对人(H2H)用户共存的LTE网络系统是必要且至关重要的。在本文中,我们提出了一种基于认知的访问策略,该策略具有基于优先级的排队模型,该模型针对具有M2M / H2H共存的LTE设计,其中M2M通信具有实时(M2M-RT)和非实时(M2M- NRT)流量。无线电访问赋予H2H最高的优先级,而M2M-RT的优先级高于M2M-NRT。建立了一个连续时间马尔可夫链模型来评估系统性能,包括服务完成率,阻塞和强制终止概率以及M2M流量的平均排队延迟。此外,还评估了M2M流量的资源利用率。分析结果表明,所提出的排队模型在保护H2H服务的同时,可以增加M2M流量网络的容量,同时降低阻塞概率。此外,允许将中断的M2M-NRT重新插入其队列中可以进一步降低强制终止率。由于这些原因,可以得出结论,所提出的模型可以用于改善多通道M2M通信网络的系统性能。

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