Topics of Interest

This workshop presents state-of-the-art research in machine learning for networking. Both theoretical and system papers will be considered, to present novel contributions in the field of machine learning, deep learning and, in general, network intelligent tools, including scalable analytic techniques and frameworks capable of collecting and analyzing both online and offline massive datasets, open issues related to the application of machine learning into communications and networking problems and to share new ideas and techniques for machine learning in communication systems and networks. The topics of interest include (but not limited to):

  • Deep and Reinforcement learning for networking and communications in networks

  • Data mining and big data analytics in networking

  • Protocol design and optimization using AI/ML

  • Self-learning and adaptive networking protocols and algorithms

  • Intent & Policy-based management for intelligent networks

  • Innovative architectures and infrastructures for intelligent networks

  • AI/ML for network management and orchestration

  • AI/ML for network slicing optimization in networking

  • AI/ML for service placement and dynamic Service Function Chaining

  • AI/ML for C-RAN resource management and medium access control

  • Decision making mechanisms

  • Routing optimization based on flow prediction network systems

  • Bio-inspired learning for networking and communications

  • Protocol design and optimization using machine learning

  • Data analytics for network and wireless measurements mining

  • Big data analysis frameworks for network monitoring data

  • Methodologies for network problem diagnosis, anomaly detection and prediction
  • Network Security based on AI/ML techniques

  • AI/ML for multimedia networking

  • AI/ML support for ultra-low latency applications

  • AI/ML for IoT

  • Open-source networking optimization tools for AI/ML applications

  • Experiences and best-practices using machine learning in operational networks

  • Novel context-aware, emotion-aware networking services

  • Machine learning for user behavior prediction

  • Modeling and performance evaluation for Intelligent Network

  • Intelligent energy-aware/green communications

  • Machine learning and data mining for networking

  • Transfer learning and reinforcement learning for networking system

  • Network anomaly diagnosis through big networking data and wireless

  • Machine learning and big data analytics for network management

  • Big data analytics and visualization for traffic analysis

  • Fault-tolerant network protocols using machine learning

  • Experiences and best-practices using machine learning in operational networks