Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 8 references
Abstract
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.
Accurate network traffic forecasting is fundamental to Quality of Service enforcement, proactive congestion control, and dynamic resource allocation in modern backbone and software-defined networks. However, existing approaches often lack adaptability to non-stationary traffic patterns and fail to provide a consistent...
E. Chithra, G. C. Bharathi, S. Allada et al.· International Conference on...· 0 citations
A Quality-of-Service (QoS)-Aware Intelligent Model Selection framework that predicts the expected QoS of candidate perception models from model, network, and hardware context and dynamically selects a feasible model according to application priorities is proposed.
S. Singh, A. Mishra· International Journal for Re...· 0 citations
Experimental results demonstrate that the proposed framework outperforms existing baseline methods in both response time and throughput prediction tasks, and the multi-task learning strategy enhances prediction accuracy while significantly improving computational efficiency.
Zhenzhen Liu· Academic Journal of Engineer...· 0 citations
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
This study addresses the challenge by evaluating a diverse spectrum of models including seasonal decomposition, Prophet, Random Forest, XGBoost, Support Vector Regression, and advanced deep learning architectures like bidirectional and Convolutional LSTMs - using a common interface dataset benchmarked across MAPE, NRMS...
Niraj Gadhe, K. Bhardwaj, M. Jain et al.· arXiv.org· 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
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