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

Submissions

Paper Submissions

We welcome the submission of full length unpublished papers on the application and theory of machine learning to communications. Below, we provide a non-exhaustive list of possible topics. We do not restrict the type of machine learning techniques, or the area of application within communications systems.

  • Machine learning driven design and optimization of modulation and coding schemes
  • Machine learning techniques for channel estimation, channel modeling, and channel prediction.
  • Machine learning based enhancements for difficult to model communications channels such as molecular, biological, multi-scale, and other non-traditional communications mediums
  • Transceiver design and channel decoding using deep learning
  • Machine learning driven techniques for radio environment awareness and decision making
  • Machine learning for Internet of things (IoT) and massive connectivity.
  • Machine learning for ultra-reliable and low latency communications (URLLC).
  • Machine learning for Massive MIMO, active and passive Large Intelligent Surfaces (LIS).
  • Distributed learning approaches for distributed communications problems
  • (Deep) Reinforcement Learning and Policy learning for resource management & optimization
  • Reinforcement Learning for self-organized networks and AP/BTS optimization
  • Machine learning techniques for non-linear signal processing
  • Low-complexity and approximate learning techniques and power reduction applications
  • Machine learning for edge Intelligence, sensing platforms, and sense making
  • Algorithmic advances in machine learning for communication systems
  • Advancing the joint understanding of information theory, capacity, complexity and machine learning communications systems
  • Machine learning methods for exploiting complex spatial, traffic, channel, traffic, power and other distributions more effectively using measurement vs idealized distributions.
  • Compression of neural networks for low-complexity hardware implementation
  • Unsupervised, semi-supervised and self-supervised learning approaches to communications

Please submit your papers in accordance with IEEE ICC workshop paper standards via EDAS at https://edas.info/newPaper.php?c=26827

Demonstration & Exhibition Submissions

We also invite the submission of 1-3 page extended abstracts for the presentation of demonstrations of machine learning based communications systems, prototypes and other real-world demonstrations which fall within the scope of this workshop.   Submission information for these will be available shortly.

Patrons

Exhibitors

Supporters