Throughput and Age of Information in a Cellular-Based IoT Network

计算机科学 吞吐量 公制(单位) 泊松点过程 计算机网络 蜂窝网络 性能指标 基站 物联网 泊松分布 无线 电信 数学 统计 嵌入式系统 运营管理 管理 经济
作者
Praful D. Mankar,Zheng Chen,Mohamed A. Abd-Elmagid,Νικόλαος Παππάς,Harpreet S. Dhillon
出处
期刊:IEEE Transactions on Wireless Communications [Institute of Electrical and Electronics Engineers]
卷期号:20 (12): 8248-8263 被引量:62
标识
DOI:10.1109/twc.2021.3091540
摘要

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the Johnson-Mehl (JM) tessellation, such that the IoT devices located in the JM cells are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.
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