IEEE Wireless Communications and Networking Conference
10–13 April 2022 // Austin, TX, USA
Boosting Verticals into Wireless Orbit

Call for Papers

We invite submissions of unpublished works on the application and theory of machine learning to communications systems. 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 driven novel waveforms and communications methods
  • Machine learning driven methods and concepts to drive 5.5G and 6G systems
  • 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, 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.
  • 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

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