Open Workshop on Machine Learning in Communications (OpenMLC)
IEEE GLOBECOM 2020 Workshop 12 – Date: 11 December 2020 (Friday)
Call for Workshop Papers
We invite submissions of unpublished works on the application and theory of machine learning to communications.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.
- 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).
- Machine learning for vision-aided wireless communications
- 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
- 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.
- Applications of transfer learning in wireless communication
- Compression of neural networks for low-complexity hardware implementation
- Unsupervised, semi-supervised and self-supervised learning approaches to communications
- Machine learning framework for joint communication, control, and security & privacy
- Privacy-preserving distributed machine learning for communications networks
PAPER SUBMISSIONS
Please submit your papers in accordance with IEEE GLOBECOM workshop paper standards via EDAS at https://edas.info/N27528
IMPORTANT DATES
- Paper Submission Deadline: 14 August 2020
- Paper Acceptance Notification: 15 September 2020
- Camera-Ready: 1 October 2020
