We welcome the submission of full length unpublished papers on the application and theory of machine learning to communications.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.