Call for Papers

New generations of wireless systems empowered by cognitive radio technologies can learn from spectrum environment and optimize themselves for a more efficient use of limited spectrum resources. Wireless communications combine various waveform, channel, traffic, and interference effects, each with its own complex structures that quickly change over time. The data underlying wireless communications comes in large volumes and at high rates, and is subject to harsh interference and security threats due to the open and broadcast nature of wireless medium. Traditional modeling and protocol tuning techniques often fall short of capturing the delicate relationship between the RF spectrum dynamics and communication design. To fill this gap, machine learning provides automated means to learn from and adapt to spectrum dynamics. Operating on raw RF data, machine learning promises to meet speed, reliability, and security requirements of wireless communication systems by revealing the complex generative processes behind the data. Applications of machine learning to wireless communications span the full network protocol stack and provide new design opportunities to push the performance boundaries of wireless communications. The potential benefits of machine learning are to be realized by the emerging computational platforms and programming capabilities specialized to answer wireless communications and networking needs. With the increasing use of machine learning in wireless communication systems, it is also critical to understand the security implications of machine learning. In this context, adversarial machine learning has emerged as a field to study learning in the presence of an adversary and support safe adoption of machine learning to the emerging applications in the wireless domain. The purpose of this symposium is to bring together leading researchers in the theory, design, and implementation of machine learning for wireless communications, networking and security. This symposium will provide an environment to foster collaboration in these timely research areas.

Topics of interest include (but are not limited to):

  • Machine learning for wireless communications
  • Machine learning for wireless signal analysis and spectrum characterization
  • Machine learning for situational awareness in wireless networks
  • Machine learning for design and optimization of wireless network protocols
  • Machine learning for cognitive radio and radar
  • Machine learning for 5G
  • Machine learning for wireless networks and applications
  • Machine learning for wireless security
  • Machine learning for Internet of Things (IoT) and mobile edge/fog computing
  • Adversarial machine learning for wireless systems
  • Computational aspects of machine learning in wireless systems
  • Hardware and software for wireless applications with machine learning
  • Datasets, experiments, and testbeds for wireless systems with machine learning

Distinguished Symposium Talks:

  • Prof. Danijela Cabric (University of California, Los Angeles)
  • Prof. Deniz Gunduz (Imperial College)

Paper Submission: 

Prospective authors are invited to submit full-length papers (up to 4 pages for technical content including figures and possible references, and with one additional optional 5th page containing only references) and extended abstracts (up to 2 pages, for paper-less industry presentations and Ongoing Work presentations) via Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. The accepted extended abstracts will not be indexed in IEEE Xplore, however the abstracts and/or the presentations will be included in the IEEE SPS SigPort. Accepted papers and abstracts will be scheduled in lecture and poster sessions. We are also inviting industry talks. Please contact the Symposium Chairs if you are interested in giving an industry talk. 


Important Dates:

June 27, 2019: Paper submissions due

July 15, 2019: Notification of Acceptance

August 15, 2019: Camera-ready paper due


Symposium Chairs:

Dr. Silvija Kokalj-Filipovic (Perspecta Labs)

Dr. Tim O'Shea (DeepSig and Virginia Tech)

Dr. Yalin Sagduyu (Intelligent Automation, Inc.)

Dr. Yi Shi (Virginia Tech and Intelligent Automation, Inc.)

Dr. George Stantchev (Naval Research Laboratory)

Dr. Osman Yagan (Carnegie Mellon University)