## 1 Introduction
In this context, a comparative analysis of these options is provided as well, assessing their feasibility according to different criteria, including technical, regulatory, and business aspects. In [21], the impact of 5G networks in a manufacturing environment is analyzed. To this end, key feasibility features related to scalability, security, and data analytics are described. In [22], the authors provide an integrated methodology and approach for benchmarking and profiling 5G vertical industries' applications. To this end, this approach will facilitate the development of artificial intelligence (AI) models in industrial environments. In [23], the authors present a time sensitive networking (TSN) implementation for a 5G network operating at 28 GHz, in the context of an NPN. Moreover, NPN and TSN assisted 5G catering for smart systems, such as the smart home or vehicle, is emulated. In parallel, there is a growing interest towards the adoption and implementation of open solutions and interfaces towards the design of NPNs in order to leverage the reusability and scalability of private 5G architectures. This is expected to leverage the concept of cell densification, as previously described. A typical example is the Open RAN (O-RAN) alliance, primarily aiming at the adoption of standards that are fully supporting and complimentary to the ones promoted by 3 GPP and other industry standard organizations [24]. Building on the principles of openness and disaggregation of the RAN component, O-RAN deployments will be crucial to overcome the current issues with vendor lock-in and are expected to be the key pillar for the realization of cost-efficient and scalable NPNs. At the same time, additional key enabling technologies, including (i) the support of data analytics and AI/ML-based network optimization, (ii) RAN sharing and neutral hosting, (iii) resource and service orchestration over heterogeneous infrastructures, as well as (iv) the support of TSN, particularly for industrial applications, will primarily drive the technological evolutions in NPN design and deployment. Driven by the above-mentioned considerations, the goal of this paper is to propose an architectural approach for the deployment and support of 5G PNs. This architecture is fully exploitable and open by adopting RAN functions on open interfaces and standard hardware platforms. At the same time, the support of innovative verticals is achieved through cloud native orchestrators, managing the various IoT elements and customized devices, as well as the migration of virtual network functions (VNFs) across different network nodes [25]. In the proposed system, intelligent management of the network, the infrastructure resources, and the services are facilitated by AI and machine learning (ML) algorithms and the provision of data analytics from all architectural layers. The overall goal is to provide cost-efficient deployments of 5G PNs able to support a variety of applications and use cases with distinct characteristics in terms of throughput, latency, number of supported user equipments (UEs) and sensitivity to timing inaccuracies, among others. In addition, to address the diverse requirements of different types of 5G services, the system includes a network slicing component, which can exploit AI/ML functionality and provide end-to-end (E2E) slices in the compute, network, and access network domains. Targeting PNs with reduced deployment and operational costs, our system also supports neutral host RAN sharing. The required resource and service orchestration in our approach is undertaken by an E2E solution operating on top of 5G infrastructures, composed of heterogeneous components like virtual network functions, multi-access edge computing (MEC) resources and hardware devices. The orchestration solution is aligned with the latest O-RAN specifications and compatible with the relevant interface for managing the O-Cloud infrastructures.