Jul 2026· International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence· 0 citations· 19 references
TL;DR
The study concludes that rule-based approaches remain highly valuable for mission-critical 5G slicing applications and recommends their integration with adaptive techniques to enhance scalability and performance.
Abstract
The development of 5G technology has brought about network slicing as a key architectural advancement, allowing multiple virtual networks to function over a single shared physical infrastructure. Accurate classification of traffic into suitable slices enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC) is essential to ensure Quality of Service (QoS) and efficient resource utilization. While recent research has largely focused on machine learning and deep learning techniques, challenges such as lack of explainability, high computational cost, and limitations in real-time implementation have renewed attention toward deterministic, rule-based methods. This study conducts a systematic conceptual review of rule-based prediction models for 5G network slicing classification using the PRISMA framework. A comprehensive search of peer-reviewed studies published between 2022 and 2025 was performed across major academic databases. After undergoing identification, screening, eligibility evaluation, and final inclusion processes, a total of 30 relevant studies were analyzed. The results show that rule-based models offer advantages such as interpretability, low-latency decision-making, support for regulatory compliance, and strong suitability for deployment in edge computing environments. Based on these findings, a structured Rule-Based Prediction Model (RBPM) framework is proposed. The study concludes that rule-based approaches remain highly valuable for mission-critical 5G slicing applications and recommends their integration with adaptive techniques to enhance scalability and performance.
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
A slice-aware deep learning framework for the joint prediction of traffic demand and multiple KPIs within a simulation-driven environment and shows that deep learning models more effectively capture nonlinear slice-level dynamics compared with traditional forecasting approaches.
Sultan Ertas, B. Cavusoglu· IEEE Access· 0 citations
This study presents a comparative machine learning analysis for modeling decision-making processes in dynamic network slicing within 6G networks. Experiments use the 6G Network Slicing QoS Dataset containing network telemetry and Quality of Service (QoS) indicators. Two tasks are considered: (i) prediction of the resource allocation decision and (ii) multi-class classification of the operational network state. Several models, including Random Forest, Gradient Boosting, XGBoost, CatBoost, LightGBM, ExtraTrees, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM), are evaluated under a unified experimental protocol. Performance is measured using accuracy, precision, recall, and macro-F1 metrics. Results indicate that tree-based ensemble methods achieve strong predictive performance for both resource allocation decisions and QoS-driven network state modeling. While several ensemble learning methods achieved near-perfect performance in the resource allocation task, LightGBM achieved the best results in the network state classification task with 0.99 accuracy and 0.98 macro-F1.
Amine Gonca Toprak, Ö. B. Mercan· Signal Processing and Commun...· 0 citations
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
S. Alanazi· Journal of Advances in Infor...· 0 citations
Network slicing is an enabling technology of fifth-generation (5G) mobile networks that enables several autonomous logical networks to exist on a common physical infrastructure. One key issue of the paradigm is the admission control mechanism which slice requests are accepted to achieve the best performance of the system and still ensure quality-of-service (QoS) guarantees. In this paper, the authors provide a comparative study of the current methods of admission control and introduce a new approach, Priority-Aware Deep Q-Network with Dynamic Threshold Adaptation (PA-DQN-DTA). Our method (as opposed to the traditional methods which tie admission control to resource allocation) addresses intelligent admission decisions only. The suggested scheme uses inter-slice and intra-slice priorities via a mathematically defined admission probability functional. The outcomes of simulation in four assessment scenarios show that parameter calibration is of the essence: the balanced configuration reaches acceptance ratios of 11.812.9, and QoE in all scenarios is above 98.8%. Besides, this paper presents an in-depth analysis of parameter tuning and describes the space of tradeoffs between the acceptance ratio, the QoE preservation, and the resource usage. Concrete recommendations are made and implications to the realistic 5G deployment are discussed.
A. S. Mahore, C. N. Deshmukh· International Conference Com...· 0 citations
The increasing complexity and volume of mobile network traffic present significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. Accurate traffic classification enables application-aware resource allocation by distinguishing applications with different bandwidth, latency, and reliability requirements. Traditional classification techniques, including port-based identification and Deep Packet Inspection (DPI) have become inadequate and less effective due to widespread encryption, port masquerading, and growing privacy concerns. This paper presents a supervised learning-based approach for application-level network traffic classification specifically as a foundation for QoS optimization in future 5G networks. Since publicly available labeled 5G traffic datasets remain limited, this study uses the MIRAGE-2019 mobile traffic dataset as a proxy dataset to evaluate the proposed classification framework. A Random Forest classifier was implemented using flow-level statistical features extracted from the mobile application traffic. The framework further incorporates a rule-based QoS policy mapping informed by RFC 4594 DiffServ service class guidelines to assign application-specific priority levels, bandwidth requirements, latency sensitivity, and jitter tolerance. Experimental evaluation achieved an overall classification Accuracy of 71.83%, a Macro F1-score of 0.6701, and a Weighted F1-score of 0.7227 across twenty mobile applications. Although the experiments were conducted using a pre-5G mobile traffic dataset, the results demonstrate that supervised machine learning can effectively classify encrypted mobile application traffic and provide a practical foundation for application-aware QoS policy enforcement in future 5G and next-generation mobile networks.
Mohammed Aqeel Ismail, Okuthe P. Kogeda· Computers· 0 citations