Varvara Manaeva

Enabling efficient traffic coexistence between ultra-reliable low-latency (URLLC) and enhanced mobile broadband (eMBB) services at the 5G New Radio (NR) air interface using the concept of network slicing requires careful tuning of session and medium access control (MAC) layer parameters. The studies performed so far addressed only one of these layers concentrating either on resource allocation or packet scheduling. The aim of this paper is to propose a joint model for performance analysis and optimization of session and MAC layer parameters jointly. To this aim, by accounting for wireless channel characteristics and arriving traffic specifics at both session andMAC layers, we utilize the tools for stochastic geometry and queuing theory to formulate a joint performance model. The model has a two-level structure, where the solution of the session level sub-model provides the input to the MAC one. As the intermediate parameters connecting two levels we consider the URLLC and eMBB drop and preemption probabilities that allows us to characterize the input traffic at the MAC layer. The ultimate metric of interest is the delay of the URLLC and eMBB packets experienced in the MAC buffer. Our numerical results show that the use of strict connection admission control at the session layer leads to the pessimistic system behavior at the MAC layer making the system overprovisioned in terms of packet loss probabilities, that is, the worse case packet loss probability for eMBB traffic is 10⁻⁴. While it ensures close to 1 ms packet latency for URLLC traffic over 90% admitted range of session arrival intensities, it is still violates it at the maximal admitted rates increasing the URLLC packet delay up to ≈ 3 ms. Thus, additional degree of overprovisioning is required on top of conventional prioritization at the session level.

Nowadays, Internet of Things (IoT) is one of the most dynamically evolving services in the 5G ecosystem. In industrial IoT (IIoT), this service can be utilized to deliver state updates of various equipment to the remote control center for further coordination and maintenance. As a result, one of the critical metrics of interest for such a service is the Age of Information (AoI) and its upper bound—peak AoI (AoI)—characterizing the freshness of information about the state of the systems. In spite of significant attention, these metrics received over the last decade, only little is known regarding the PAoI performance of a single source (e.g., sensor) in the presence of competing traffic from other sources in queuing systems. On top of this, models with batch arrivals and batch services that can be effectively used to represent service performance in modern cellular systems such as 5G New Radio are lacking. In our study, we consider a cellular air interface representing it as a queuing system (QS) in discrete-time with batch arrivals and service and investigate performance of a single (tagged) source in presence of competing traffic from other sources having the same priority, where all the sources are modeled using the switched Poisson process (SPP) characterized by sophisticated correlational properties. We also investigated the impact of several service disciplines on the performance of the tagged source including first-come–first-served (FCFS), last-come–first-served (LCFS), random, and priority-based service. Our results illustrate that, although the qualitative behavior of the mean PAoI is different for different service disciplines, the optimal value of PAoI is insensitive to the choice of the service order. On top of this, we observed that introducing a priority in service to one of the flows may drastically affect the performance of other flows even when the overall load contribution of a single flow is rather limited. Our observations can be utilized to design packet scheduling strategies for 4G/5G cellular systems carrying traffic of state update applications.

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.