Fifth Generation (5G) is here to accelerate the digitization of economies and society, and open up innovation opportunities for verticals. A myriad of 5G-enabled use cases has been identified across disparate sectors like tourism, retail industry, and manufacturing. Many of the networks of these use cases are expected to be private networks, that is, networks intended for the exclusive use of an enterprise customer. This article provides an overview of the technical aspects in private 5G networks. We first identify the key requirements and enabling solutions for private 5G networks. Then, we review the latest 3rd Generation Partnership Project (3GPP) Release 16 capabilities to support private 5G networks. Next, we provide architecture proposals for single site private networks, including the scenario in which the radio access network (RAN) is shared. Afterwards, we address mobility and multi-site private 5G network scenarios. Finally, we identify key challenges for private 5G networks.

Network slicing is a powerful paradigm for network operators to support use cases with widely diverse requirements atop a common infrastructure. As 5G standards are completed, and commercial solutions mature, operators need to start thinking about how to integrate network slicing capabilities in their assets, so that customer-facing solutions can be made available in their portfolio. This integration is, however, not an easy task, due to the heterogeneity of assets that typically exist in carrier networks. In this regard, 5G commercial networks may consist of a number of domains, each with a different technological pace, and built out of products from multiple vendors, including legacy network devices and functions. These multi-technology, multi-vendor and brownfield features constitute a challenge for the operator, which is required to deploy and operate slices across all these domains in order to satisfy the end-to-end nature of the services hosted by these slices. In this context, the only realistic option for operators is to introduce slicing capabilities progressively, following a phased approach in their roll-out. The purpose of this paper is to precisely help designing this kind of plan, by means of a technology radar. The radar identifies a set of solutions enabling network slicing on the individual domains, and classifies these solutions into four rings, each corresponding to a different timeline: (i) as-is ring, covering today’s slicing solutions; (ii) deploy ring, corresponding to solutions available in the short term; (iii) test ring, considering medium-term solutions; and (iv) explore ring, with solutions expected in the long run. This classification is done based on the technical availability of the solutions, together with the foreseen market demands. The value of this radar lies in its ability to provide a complete view of the slicing landscape with one single snapshot, by linking solutions to information that operators may use for decision making in their individual go-to-market strategies.

The essential propellant for any closed-loop management mechanism is data related to the managed entity. While this is a general evidence, it becomes even more true when dealing with advanced closed-loop systems like the ones supported by Artificial Intelligence (AI), as they require a trustworthy, up-to-date and steady flow of state data to be applicable. Modern network infrastructures provide a vast amount of disparate data sources, especially in the multi-domain scenarios considered by the ETSI Industry Specification Group (ISG) Zero Touch Network and Service Management (ZSM) framework, and proper mechanisms for data aggregation, pre-processing and normalization are required to make possible AI-enabled closed-loop management. So far, solutions proposed for these data aggregation tasks have been specific to concrete data sources and consumers, following ad-hoc approaches unsuitable to address the vast heterogeneity of data sources and potential data consumers. This paper presents a model-based approach to a data aggregator framework, relying on standardized data models and telemetry protocols, and integrated with an open-source network orchestration stack to support their incorporation within network service lifecycles.

The on-going digital transformation is key to progress towards a new generation of more efficient, sustainable and connected industrial systems allowing the so-called factories of the future. This new generation, commonly referred to as industry 4.0, will be accompanied by a new wave of use cases that will allow companies from logistics and manufacturing sectors to increase flexibility, productivity and usability in the industrial processes executed within their factory premises. Unlike typical use cases from other vertical sectors (e.g. energy, media, smart cities), industry 4.0 use cases will bring very stringent requirements in terms of latency, reliability and high-accuracy positioning. The combination of 5G technology with enterprise network solutions becomes crucial to satisfy these requirements in indoor, private environments. In this context, the concept of 5G non-public networks has emerged. In this article we provide an overview of 5G non-public networks, studying their applicability to the industry 4.0 ecosystem. On the basis of the work (being) developed in 3GPP Release 16 specifications, we identify a number of deployment options relevant for non-public networks, and discuss their integration with mobile network operators' public networks. Finally, we provide a comparative analysis of these options, assessing their feasibility according to different criteria, including technical, regulatory and business aspects. The outcome of this analysis will help industry players interested in using non-public networks to decide which is the most appropriate deployment option for their use cases.