Subject: Other engineering and technologies
Year: 2026
Type: Article
Type: NonPeerReviewed
Title: AI-Driven Network Optimization for the 5G-to-6G Transition: A Taxonomy-Based Survey and Reference Framework
Author: Mustafovski, Rexhep
Author: Marinova, Galia
Author: Qehaja, Besnik
Author: Hajrizi, Edmond
Author: Gagica, Shejnaze
Author: Guliashki, Vassil
Abstract: This paper presents a taxonomy-based survey of AI-driven network optimization mechanisms relevant to the transition from fifth generation (5G) to sixth generation (6G) mobile communication systems. In contrast to earlier generational shifts that are often described as technology replacement cycles, the 5G-to-6G evolution is increasingly characterized in the literature as a prolonged period of coexistence, hybrid operation, and progressive integration of new capabilities across radio, edge, core, and service layers. To structure this transition, the paper organizes prior work into a transition-oriented taxonomy covering migration strategies, AI-enabled closed-loop control, RAN disaggregation and edge intelligence, core virtualization and slice orchestration, spectrum-aware coexistence, service-driven requirements, and security-aware governance. Rather than introducing a new optimization algorithm or an experimentally validated architecture, the contribution of this survey is analytical and integrative. Specifically, it consolidates fragmented research directions into a reference view of how AI-driven control mechanisms are distributed across spectrum, RAN, edge, and core domains during hybrid 5G–6G operation. In addition, the paper includes a structured evidence synthesis of performance trends, deployment maturity signals, and recurring methodological limitations reported across the literature. The review indicates that meeting anticipated 6G objectives, including ultra-low latency, high reliability, scalability, and improved energy efficiency, depends less on isolated enhancements at individual protocol layers and more on coordinated cross-layer optimization supported by AI-native control loops. At the same time, the surveyed literature reveals persistent gaps in service-to-control mapping, security-aware orchestration, interoperability across heterogeneous domains, and reproducible evaluation methodologies for hybrid 5G–6G environments. The survey is intended to provide researchers, network operators, and standardization stakeholders with a structured analytical basis for assessing how AI-driven optimization can support the staged evolution from 5G systems toward 6G-ready infrastructures.
Publisher: MDPI
Relation: https://eprints.ugd.edu.mk/38187/
Identifier: oai:eprints.ugd.edu.mk:38187
Identifier: https://eprints.ugd.edu.mk/38187/1/MSc.%20Rexhep%20Mustafovski.pdfIdentifier: Mustafovski, Rexhep and Marinova, Galia and Qehaja, Besnik and Hajrizi, Edmond and Gagica, Shejnaze and Guliashki, Vassil (2026) AI-Driven Network Optimization for the 5G-to-6G Transition: A Taxonomy-Based Survey and Reference Framework. Future Internet, 18 (3): 4182079. pp. 1-24. ISSN 1999-5903
Identifier: https://doi.org/10.3390/fi18030155
