Performance Analysis and Uplink Scheduling for QoS-Aware NB-IoT Networks in Mobile Computing
Performance Analysis and Uplink Scheduling for QoS-Aware NB-IoT Networks in Mobile Computing
复制标题
移动计算中 QoS 感知 NB-IoT 网络的性能分析和上行链路调度
DOI：
10.1109/access.2019.2908985
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Xiangkun
中科院分区：
文献类型：
--
作者：
Chen, Xin;Li, Zhuo;Wang, Xiangkun
近年来，随着第五代演进（5G）、物联网（IoT）和移动的计算的快速增长，低功率广域（LPWA）通信获得了越来越多的关注。窄带物联网（NB-IoT）是一种基于蜂窝物联网的LPWA技术，其支持海量连接、广域覆盖、超低功耗和超低成本。NB-IoT通信的研究越来越有吸引力。网络演算理论有助于NB-IoT系统的性能分析和网络优化。我们旨在分析和优化NB-IoT网络。在本文中，我们构建了一个随机接入业务模型，包括NB-IoT用户设备（UE）的到达过程和eNB的服务过程。然后，我们利用随机网络演算（SNC）分析NB-IoT流量模型中的网络延迟。在不同的到达过程中的随机延迟界限。仿真结果表明，SNC可以有效地评估不同分布下的系统时延。针对多个UE同时接入服从Beta分布的情况，首先提出了一种改进的K-means算法对NB-IoT终端进行聚类。在此基础上，提出了基于优先级的调度策略。它由优先级生成算法IPGNTQ和NB-IoT任务调度算法SANTQ组成。大量的实验结果表明，我们提出的优化策略可以有效地缓解网络拥塞。此外，我们将我们提出的优化方案与四种现有的上行链路流量调度方案进行了比较，结果表明我们的方案优于所有这些方案。
Low-power wide-area (LPWA) communication has gained increasing attention in recent years with the rapid growth of fifth generation evolution (5G), the Internet of Things (IoT), and mobile computing. Narrowband Internet of Things (NB-IoT) is one kind of LPWA technology based on cellular IoT, which supports massive connections, wide area coverage, ultra-low power consumption, and ultra-low cost. Research on NB-IoT communication is increasingly attractive. Network calculus theory facilitates the performance analysis and network optimization of the NB-IoT system. We aim to analyze and optimize the NB-IoT networks. In this paper, we construct a random access traffic model including the NB-IoT user equipment (UE) arrival process and eNB service process. Then, we utilize the stochastic network calculus (SNC) to analyze the network delay in NB-IoT traffic model. Random latency bounds in different arrival processes are derived. Simulations show that SNC can evaluate the system delay under different distributions effectively. For the condition that numerous UEs access simultaneously following the Beta distribution, we first propose an improved K-means algorithm to cluster the NB-IoT terminals. Then, we raise the scheduling strategy on the basis of priority. It consists of the priority generation algorithm IPGNTQ and the NB-IoT task scheduling algorithm SANTQ. The extensive experiment results verify that our proposed optimized strategy can alleviate the network congestion effectually. Moreover, we compare our proposed optimized scheme with four existing uplink traffic scheduling schemes, showing that ours outperforms all of them.