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We invite submissions of unpublished works on the application and theory of machine learning to communications systems.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