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Data Traffic Model in Machine to Machine Communications over 5G Network Slicing

机译:5G网络切片中机器对机器通信中的数据流量模型

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摘要

The recent advancements in cellular communication domain have resulted in the emergence of Machine-to-Machine applications, in support of the wide range and coverage provision, low costs, and high mobility. 5G network standards represent a promising technology to support the future of Machine-to-Machine data traffic. In recent years, Human-Type-Communication traffic has seen exponential growth over cellular networks, which resulted in increasing the capacity and higher data rates. These networks are expected to face challenges such as explosion of the data traffic due to the future of smart devices data traffic with various Quality of Service requirements. This paper proposes a novel data traffic aggregation model and algorithm along with a new 5G network slicing based on classification and measuring the data traffic to satisfy Quality of Service for smart systems in a smart city environment. In our proposal, 5G radio resources are efficiently utilized as the smallest unit of a physical resource block in a relay node by aggregating the data traffic of several Machine-to-Machine devices as separate slices based on Quality of Service for each application. OPNET is used to assess the performance of the proposed model. The simulated 5G data traffic classes include file transfer protocol, voice over IP, and video users.
机译:蜂窝通信领域的最新进展导致出现了机器对机器应用程序,以支持广泛的范围和覆盖范围,低成本和高移动性。 5G网络标准代表了一种有前途的技术,可支持机器对机器数据流量的未来。近年来,人类类型通信流量在蜂窝网络上呈指数增长,这导致容量增加和数据速率提高。预计这些网络将面临诸如由于具有各种服务质量要求的智能设备数据流量的未来而导致数据流量爆炸的挑战。本文提出了一种新颖的数据流量汇聚模型和算法,以及一种基于分类和测量数据流量的新5G网络切片,以满足智能城市环境中智能系统的服务质量。在我们的建议中,通过基于每个应用程序的服务质量,将几个机器对机器设备的数据流量聚合为单独的切片,将5G无线电资源有效地用作中继节点中物理资源块的最小单元。 OPNET用于评估所提出模型的性能。模拟的5G数据流量类别包括文件传输协议,IP语音和视频用户。

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    Lee GM;

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  • 年度 100
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