Welcome to the Workshop on Machine Learning for Signal Processing in Wireless Communications, Sensing and Radar at IJCAI 2019 in Macao, China

Artificial Intelligence (AI) and Machine Learning (ML) approaches, well known from Computer Science disciplines, are beginning to emerge in the RF Signal Processing, Communications and Networking domains. However, there are various challenges arising in the application of Machine Learning to RF signals, such as inherently high data rates, sensitivity to environmental effects (noise, multi-path, interference etc), presence of multi-scale features in both frequency and time domains, to name a few. Also, in contrast to the image and text processing domains, the scarcity of large public repositories of standardized RF signal data makes it harder for academic and industry researchers to test and validate their algorithms in a robust, reproducible, and scalable fashion. The goal of this workshop is to bring together researchers from the RF Signal Processing and Machine Learning communities, showcase state-of-the-art Machine Learning approaches applicable in the RF domain, and provide a forum for discussing cross-disciplinary ideas to address present and future challenges.

Topics of interest include, but are not limited to:

  • Machine Learning for blind channel and signal characterization
  • Machine Learning for source separation
  • Machine Learning for RF signal classification
  • Machine Learning for cognitive radio communications, for instance spectrum awareness, or ptimization of spectrum usage dynamics and spectrum access control
  • Quality of unsupervised learning with corrupted, censored and missing spectrum sensing samples
  • Privacy-preserving Machine Learning for cognitive radio communications, for instance in 5G cellular networks
  • Machine Learning for RF-based geo-location
  • Distributed learning in collaborative autonomous networked multi-agent systems
  • Adversarial Machine Learning techniques applied to RF systems
  • Machine Learning techniques for RF systems security
  • Reinforcement learning in wireless communication and sensor networks
  • Transfer Learning for wireless communication and sensor networks
  • Visual analysis of learned features in Deep Learning for RF signal processing
  • Machine Learning techniques for communications and sensing convergence

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