article · IET Networks
As mobile communications shift from 4G to 5G and look towards beyond 5G and 6G architectures, networks must support diverse use cases that require dynamic, transparent, and differentiated services. These growing demands and increasingly complex network infrastructures create major difficulties for resource management and service orchestration. Network slicing offers a viable solution by enabling efficient resource allocation and self-service features. To implement network slicing successfully, robust algorithms are needed to ensure optimal distribution of network resources. While machine learning and artificial intelligence help analyse vast datasets and support automated decision-making, certain machine learning techniques struggle to adapt to the rapidly shifting conditions of next-generation networks. This work examines the challenges tied to resource allocation and dynamic network slicing orchestration across evolving mobile systems, identifying future research directions to boost network efficiency through emerging technical approaches.
Next-generation mobile networks must support everything from connected devices to high-bandwidth consumer services without failing. Understanding how to manage network resources and automate network slicing using artificial intelligence helps ensure telecommunications infrastructure remains reliable, efficient, and capable of adapting to rising data demands.
The work focuses on conceptual challenges and research directions for telecommunications operators managing 5G and future 6G infrastructures. Because the study reviews existing challenges and outlines future research avenues rather than presenting a deployed or tested tool, it represents early-stage conceptual research that is distant from direct commercial application.
AI-generated from the published abstract. Always read the original work before citing.
Abstract The advent of 5G networks has precipitated an unparalleled surge in demand for mobile communication services, propelled by the advent of sophisticated wireless technologies. An increasing number of countries are moving from fourth generation (4G) to fifth generation (5G) networks, creating a new expectation for services that are dynamic, transparent, and differentiated. It is anticipated that these services will be adapted to a multitude of use cases and will become a standard practice. The diversity of these use cases and the increasingly complex network infrastructures present significant challenges, particularly in the management of resources and the orchestration of services. Network Slicing is emerging as a promising approach to address these challenges, as it facilitates efficient Resource Allocation (RA) and supports self‐service capabilities. However, effective network segmentation implementation requires the development of robust algorithms to guarantee optimal RA. In this regard, artificial intelligence and machine learning (ML) have demonstrated their utility in the analysis of large datasets and the facilitation of intelligent decision‐making processes. However, certain ML methodologies are limited in their ability to adapt to the evolving environments characteristic of 5G networks and beyond (B5G/6G). This paper examines the specific challenges associated with the evolution of 5G and B5G/6G networks, with a particular focus on ML solutions for RA and dynamic network slicing orchestration requirements. Moreover, the article presents potential avenues for further research in this domain with the objective of enhancing the efficiency of next‐generation mobile networks through the adoption of innovative technological solutions.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1049/ntw2.70002
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
