MLCN 2019 - Machine Learning for Communications and Networking (MLCN)

Machine learning has shown significant potentials in facilitating human-centered cognitive systems,
including image/video recognition, natural language and text analysis, finance, economics, and market
analysis, medical diagnostics, robotics and autonomous vehicle, computational biology, cyber security,
among others. The same potential can be expressed to enable the design and operations of complex
communication and networking systems that connect massive number of users with diversified quality of
service requirements. The emerging big data analytics, cloud/edge computing, software defined
networking, and other relevant technologies provide opportunities for applying machine learning in
communication and networking systems by studying the behaviors of these systems and further
improving their performance and manageability. With machine learning, communication and networking
systems can become cognizant, implementing agile reconfiguration and optimization processes based on
measured data. As a result, the users can receive a better service, as enabled by the learning of the
operating network environment and by the continuous adaptation of communication and network
parameters as the observed conditions evolve.
The Machine Learning for Communication and Networking (MLCN) Symposium will focus on topics
related to all aspects of machine learning applied to communication and networking systems. This
symposium seeks original unpublished papers focusing on theoretical analysis, algorithm/protocol design,
novel system architectures, experimental studies, emerging applications, standardizations, testbeds, etc.
The ultimate goal of this symposium is to bring together and disseminate the latest developments and
technical solutions concerning all facets of the broad area of machine learning for communication and
networking systems, including emerging intelligent and/or self-aware communications and networking
technologies to improve network resource utilization and optimization, and make future communication
and networking systems intelligent, autonomous, efficient, and trustworthy.
The ICNC’19 Machine Learning for Communication and Networking (MLCN) symposium calls for
original, previously unpublished papers in the topics including, but not limited to, the following:
● Machine learning for communication and network operation and control
● Machine learning for communication and network resource optimization
● Machine learning for cognitive communication and networks architecture
● Machine learning for communication and network security management
● Machine learning for self-aware network management
● Machine learning for the Internet of Things
● Machine learning for cyber-physical systems
● Machine learning-enabled communication and network big data analytics
● Machine learning-enabled cloud/edge/fog computing for communication and networking systems
● Machine learning-driven communication network theory and algorithms
● Machine learning for RF signal processing
● Machine learning for collaborative spectrum sharing
● Machine learning for distributed communications and sensing
● Machine learning for next-generation cognitive networks
● Machine learning for next-generation wireless networks such as 5G networks
● Machine learning for new network architectures such as software-defined networking and network
function virtualization
● Machine learning for constrained networks such as sensor networks, tactical networks, etc.
● Machine learning for supporting ultra-low latency and highly reliable communications