Beyond private 5G networks: applications, architectures, operator models and technological enablers

Abstract

Private networks will play a key role in 5G and beyond to enable smart factories with the required better deployment, operation and flexible usage of available resource and infrastructure. 5G private networks will offer a lean and agile solution to effectively deploy and operate services with stringent and heterogeneous constraints in terms of reliability, latency, re-configurability and re-deployment of resources as well as issues related to governance and ownership of 5G components, and elements. In this paper, we present a novel approach to operator models, specifically targeting 5G and beyond private networks. We apply the proposed operator models to different network architecture options and to a selection of relevant use cases offering mixed private–public network operator governance and ownership. Moreover, several key enabling technologies have been identified for 5G private networks. Before the deployment, stakeholders should consider spectrum allocation and on-site channel measurements in order to fully understand the propagation characteristic of a given environment and to set up end-to-end system parameters. During the deployment, a monitoring tools will support to validate the deployment and to make sure that the end-to-end system meet the target KPI. Finally, some optimization can be made individually for service placement, network slicing and orchestration or jointly at radio access, multi-access edge computing or core network level.

6G goal-oriented communications: How to coexist with legacy systems?

“…. Finally, by minimizing (29) in a per-slot basis, we obtain the following instantaneous problem (we omit the constant terms): (30) as described in Section IV.A 3) Observe the real goal value outcome at the output of the classifier (( 14)), and update the virtual queue Z t as in (28) 4) Go to next time slot t + 1 observations of context parameters and properly defined state variables (virtual queues). The virtual queue (which evolves over time) and the parameter Ω drive the trade-off between U E d data rate loss (i.e., the goal cost), U E g goaleffectiveness, and convergence time.…”

“…Let us notice that the first indicator function in (30) (1 {Θt≥Θ th } ) is strongly dependent on the choice of the PER (equivalently, in practice, on the specific MCS employed for communication at time slot t). It should be noted that, in general, there is no known function relating PER and entropy relative increase (goal value).…”

“…Therefore, the decision has to be taken on a "best guess" of what could the outcome be. For this reason, as an approximation to solve (30), we replace the first indicator function with its expectation, i.e. the probability of the goal value exceeding the threshold.…”

“…8, we show the trade-off between goal-effectiveness and goal cost, for a fixed goal-effectiveness threshold E g = 82%, and for different goal value thresholds (the same as the previous figure). Also, let us recall that our solution hinges on the problem approximation that replaces the first indicator function in (30) with its expectation computed on a validation set. Therefore, in Fig.…”

“…As a second aspect tackled in this paper, a fundamental challenge of future 6G systems is to accommodate, on the same network infrastructure, services that are heterogeneous from several perspectives: requirements, resource consumption, time-variability, etc. Therefore, while it is true, in our view, that 6G will enable new classes of GO and semantic services, it is also sure that it will keep serving classical users and/or verticals [30] (i.e., data-oriented services), which in the sequel we refer to as legacy communication or, dataoriented systems and services (in this paper, we will refer to them as DO systems for brevity). For this reason, at some point in the near future (e.g., in the next 10 years), a natural question will arise on the coexistence of GO and DO systems, to understand how one system affects the other, when they share the same network infrastructure and wireless resources.…”