IEEE Access (Jan 2024)
Performance-Utilization Trade-Offs for State Update Services in 5G NR Systems
Abstract
State update applications reporting the state of a remote system to the control center constitute a critical part of modern Internet of Things (IoT) applications. The performance of these applications is conventionally assessed using the age of information (AoI) metric to quantify the freshness of the knowledge of a remote system at a control center. However, these applications react to external environmental events, such as smart grids and industrial automation deployments, and are characterized by a high degree of variability and temporal dependence in the arrival traffic patterns. To date, no studies have reported mathematical models for the peak AoI (PAoI) assessment of state-update applications under such traffic conditions and capturing specifics of the service process of 5G New Radio (NR) systems with batch arrivals and batch service. The aim of this study is to assess the impact of these properties on the PAoI performance of modern state update applications provisioned over 5G NR systems. To this end, by accounting for the coefficient of variation and autocorrelation in the traffic arrival patterns and service specifics of 5G NR systems, we utilize queuing theory and stochastic geometry tools to quantify the mean and distribution of the PAoI and sojourn time. Our numerical results showed that the mean PAoI and sojourn time (latency) were characterized by qualitatively identical responses to the coefficient of variation and lag-1 normalized autocorrelation function (NACF). Specifically, an increase in these characteristics leads to a corresponding increase in mean PAoI and latency. However, the impact of the coefficient of variation is much more profound, increasing the PAoI metric by up to 200% as compared to the conventional Poisson process, while even extremely large values of lag-1 NACF (0.6-0.9) increases the mean PAoI by 15-20% at most. From a practical perspective, we observed that in 5G NR cellular systems, the mean PAoI increases as a function of the mean packet arrival rate at a much slower rate than the increase in resource utilization. Thus, to maximize network operator revenues, we recommend utilizing the arrival rate that maximizes the latter parameter as an operational point, as it results in just a 15-20% increase in the mean PAoI.
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