IEEE International Conference on Communications
7-11 June 2020 // Virtual Conference
Communications Enabling Shared Understanding

Call for Papers


Machine learning and data-driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. To date, most existing learning solutions for wireless networks have relied on conventional machine learning approaches that require centralizing the training data and inference processes on a single data center. However, in future intelligent wireless networks, due to privacy constraints and limited communication resources for data transmission, it is impractical for all wireless devices that are engaged in learning to transmit all of their collected data to a data center that can subsequently use a centralized learning algorithm for data analytics or network self-organization. To this end, distributed edge learning frameworks are needed, to enable the wireless devices to collaboratively build a shared learning model with training their collected data locally. For wireless communication, edge machine learning admits many use cases. For example, distributed multi-agent reinforcement learning algorithms can be used to solve complex convex and nonconvex optimization problems that arise in various use cases such as network control, user clustering, resource management, and interference alignment. Moreover, distributed federated learning enables users to collaboratively learn a shared prediction model while remaining their collected data on their devices for user behavior predictions, user identifications, and wireless environment analysis. The field of edge machine learning is still at its infancy as there are many open theoretical and practical problems yet to be addressed, for edge machine learning, in general, and for wireless communication systems, in particular.

Thus, this full-day workshop will seek to bring together researchers and experts from academia, industry, and governmental agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic. Suitable topics for this workshop include, but are not limited to, the following areas:

  • Fundamental limits of edge machine learning systems
  • Wireless network optimization for improving the performance of edge machine learning
  • Radio resource management for edge machine learning
  • Multiple access for edge machine learning
  • Data compression for edge machine learning
  • Adaptive transmission for edge machine learning
  • Techniques for wireless crowd labelling
  • Interference management in edge machine learning networks
  • Emerging theories and techniques such as age of information and blockchain for edge machine learning
  • Modeling and performance analysis of edge machine learning networks
  • Energy efficiency of implementing machine learning over wireless edge networks
  • Ultra-low latency edge machine learning
  • Data analytics driven wireless communication
  • Multi-agent reinforcement learning for intelligent network control and optimization
  • Network architectures and communication protocols for edge machine learning
  • Experimental testbeds and techniques of edge machine learning
  • Privacy and security issues of edge machine learning
  • Edge machine learning for intelligent signal processing, e.g., signal detection
  • Edge machine learning for mobile user behavior analysis and inference
  • Edge machine learning for emerging applications, e.g., vehicle to everything (V2X), UAV-enabled communication, Internet of Things, intelligent reflecting surface (IRS), Massive MIMO, virtual reality (VR), and augmented reality (AR)