IEEE Global Communications Conference
8–10 December 2020 // In-person (Taipei, Taiwan)
7-11 December 2020 // Virtual
Communications for Human and Machine Intelligence

Call for Papers

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We invite submissions of unpublished works on the application and theory of machine learning to communications.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



Please submit your papers in accordance with IEEE GLOBECOM workshop paper standards via EDAS at



  • Paper Submission Deadline: 14 August 2020
  • Paper Acceptance Notification: 15 September 2020
  • Camera-Ready: 1 October 2020