AI-Optimized Traffic Modelling and Dimensioning of 6G Networks

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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