AI-Optimized Traffic Modelling and Dimensioning of 6G Networks
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 56
Special Issue Editors
Interests: teletraffic engineering in next-generation networks (5G and 6G); performance evaluation and optimization of telecommunication networks and network simulation
Special Issues, Collections and Topics in MDPI journals
Interests: 6G networks; numerical simulation; network optimization; applications of machine learning; derivation of 6G system requirements
Special Issues, Collections and Topics in MDPI journals
Interests: telecommunication networks; Internet of Things; network security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of 6G communication networks demands intelligent traffic management strategies to handle the increasing complexity of ultra-dense, high-rate, and low-latency communications. On the same hand, AI-driven approaches offer innovative solutions by leveraging machine learning (ML), deep learning (DL), and data analytics to predict and model network traffic for optimizing resource allocation in real time. By applying AI to the development of 6G, this Special Issue aims at innovative AI methodologies, challenges, and future research directions for the traffic modelling and dimensioning of 6G communication networks. It explores how ML as well as DL techniques can optimize network resource allocation, enhance the quality of service, and predict traffic patterns with high accuracy. The issue covers topics such as AI-based network design and dimensioning, network slicing, predictive traffic analytics, traffic forecasting, dynamic spectrum management, and energy-efficient traffic optimization. Additionally, it examines the role of reinforcement learning and federated learning in the heterogeneous traffic dimensioning and modelling of 6G communication networks, including cases of vehicular and satellite communications.
Prof. Dr. Ioannis Moscholios
Dr. Dimitris Uzunidis
Dr. Panagiotis Sarigiannidis
Guest Editors
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Keywords
- AI-driven traffic management and traffic-load prediction
- 6G network modelling and dimensioning
- AI traffic modelling in 6G
- machine learning/learning models for 6G
- predictive network analytics
- AI empowered network slicing
- reinforcement learning for dynamic resource allocation
- federated learning in vehicular/satellite communications
- integration of large language models (LLMs) for network management
- hybrid techniques for traffic modelling, prediction, and optimization
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