Machine learning (ML) has been successfully applied in many fields such as speech and image recognition, computer vision, medical diagnosis, and malware detection. ML techniques may be generally classified into supervised, unsupervised, semi-supervised, and reinforcement learning. The advantages of ML include the ability to learn from input data, to adapt to dynamic environments, and to support system self-organization and optimization.

 

Next generation communication technologies are foreseen to constitute the backbone of the digital transformation of societies by connecting physical and digital words, and providing ubiquitous, ultra reliable, and low latency wired and wireless connectivity to humans and machines. As communication networks, systems, and services become more complex and connected objects become more intelligent and dynamic, conventional techniques based on a-prior network and system optimization may not be suitable. In this context, ML-driven algorithms and models can offer tremendous advantages in dealing with the increased key performance indicators, system complexity, and computation load envisaged for next generation communication technologies such as B5G and 6G mobile networks. In recent years, increased research efforts have been devoted on using ML in communication technologies ranging from the Internet of Things (IoT) to wireless communications. This includes ML and optimization for the physical layer toward higher layers, signal intelligence, spectrum sensing, and hardware implementation, among many other promising communication technologies related topics.

 

The main goal of this Special Session on Machine Learning and Optimization for Next Generation Communication Technologies is to bring together researchers and scientists from academia and industry that work in this exciting field to present and discuss their latest research results, explore new ideas and solutions, and to facilitate networking among colleagues.

 

Topics of interests include but are not limited to the following:

 

·           Advances in ML for signal processing in next generation communication technologies

·           ML-driven physical layer techniques and optimization for next generation communication networks

·           ML and optimization for resource management and medium access control in next generation communication networks

·           ML for network management, orchestration, and network slicing optimization in B5G and 6G systems

·           ML-driven distributed intelligence and optimization on the edge-to-cloud and communication-computing continuum

·           ML and optimization at the application layer, including networked extended reality, digital twins, metaverse, and user behavior prediction

·           ML-enabled cross-layer optimization for next generation communication networks

·           ML for energy-efficient operation of next generation communication technologies

·           ML-aided non-terrestrial network design and optimization

·           ML for security of next generation wireless networks

·           Applications of ML in next generation communication technologies

·           Standardization activities, testbeds, and implementation of ML methods for next generation wired and wireless communication technologies

 

Special Session Co-chairs:

·         Trung Q. Duong, Memorial University, Canada

·         Hans-Jürgen Zepernick, Blekinge Institute of Technology, Sweden

 

Submission link: https://edas.info/newPaper.php?c=31104&track=122554