Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case

Abstract

As the idea of a new wireless communication standard (5G) started to circulate around the world, there was much speculation regarding its performance, making it necessary to carry out further research by keeping in view the challenges presented by it. 5G is considered a multi-system support network due to its ability to provide benefits to vertical industries. Due to the wide range of devices and applications, it is essential to provide support for massively interconnected devices. Network slicing has emerged as the key technology to meet the requirements of the communications network. In this paper, we present a review of the latest achievements of 5G network slicing by comparing the architecture of The Next Generation Mobile Network Alliance’s (NGMN’s) and 5G-PPP, using the enabling technologies software-defined networking (SDN) and network function virtualization (NFV). We then review and discuss machine learning (ML) techniques and their integration with network slicing for beyond 5G networks and elaborate on how ML techniques can be useful for mobility prediction and resource management. Lastly, we propose the use case of network slicing based on ML techniques in a smart seaport environment, which will help to manage the resources more efficiently.

Utilidad de las tecnologías de las industria 4.0 en los smart ports

“…Through 5G wireless networks, measurements are taken to optimize processes, in the case of sensor networks and intelligent controllers are very useful in ports, as they monitor the location and removal of containers from loading and unloading processes, such as the automation of machines such as cranes (49). This technology is a multisystem support network that provides benefits to industries through its wide range of support for massively interconnected devices (50).…”

A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding

“…Network providers use extra resources to ensure node security. Constraint (10) ensures that each request node is assigned to a physical node. Constraint (11) limits the number of nodes per physical node.…”

“…Ref. [10] provides a presentation on the applications of machine learning (ML) and artificial intelligence (AI) to various network slicing solutions. The research documented a variety of ML and AI algorithms, in addition to their applications to various kinds of NS use-cases, such as resource management and mobility prediction.…”

“…N NRDistribution of nodes for each NSR 20CPU NR Distribution of CPU capacity requirement for each node of a NR U[5,25] BW NR Distribution of bandwidth requirement for each link of a NR U[5,25] SR NR Distribution of required security level for each node in real number (0-0.5)LT NR Time duration of each NR T[10,35] …”

A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources

“…These include resource allocation, slice isolation, security, RAN virtualization, feature granularity, and end-to-end (E2E) slice orchestration. Sohaib et al presented the applications of machine learning (ML) and artificial intelligence (AI) for NS solutions in [11]. Their paper listed various ML and AI algorithms, and applications for different NS use-cases such as mobility prediction and resource management.…”