National Aeronautics and Space Administration

Glenn Research Center

Call for Papers

The aim of this workshop is to bring together Academia Researchers, Engineers, Technologists, Industry Personnel, and Government agencies to discuss and present their recent advancements and future interest in the field of artificial intelligence and machine learning with applications toward the development of cognitive communication systems for aerospace.

Submission Instructions: To submit an abstract, please visit and click on SUBMIT ABSTRACT (which is visible if menu is expanded). All details and instructions are included there. Abstracts should be a minimum of 250 words, including sufficient detail to demonstrate the purpose of the paper, any preliminary results, and expected results for the final paper. If your paper is accepted for presentation, you will be required to register for the workshop prior to uploading your final manuscript. Preference will be given to full papers outlining applied cognitive technologies or novel simulations of such technology for intelligent aerospace communication systems.

Important Dates

•  Abstract Submission Deadline: March 6, 2017 (Extended – still open)
•  Abstract Acceptance / Notification: March 16, 2017 (Extended)
•  Paper Submission: April 30, 2017
•  Paper Acceptance / Notification: May 21, 2017
•  Early Registration: May 30, 2017
•  Final Registration Deadline: June 13, 2017
•  Final Manuscript Submission: June 20, 2017
•  Presentation Submission: June 23, 2017
•  Workshop: June 27-28, 2017

Areas of Interest

The areas of interest for the workshop on CCAA are:

  • Cognitive technologies supporting software-defined radios, systems including machine learning methods for self reconfiguration and autonomous performance optimization.
  • Big data analytics for transfer learning to aerospace communications.
  • Cognitive network design and optimization through distributed algorithms, learning and reasoning
  • Cognitive communication security, protocol stack adaptation and cross layering in cognitive radio systems.
  • Swarm intelligence and biological-inspired networking.
  • Smart antennas, testbeds, cognitive radio platforms and autonomous hardware controls.
  • Modeling analysis and simulation of cognitive radio technologies and network.


  • Development of cognitive engines for link management, behavior prediction and optimization.
  • Development of cognitive infrastructures for intelligent networking between ground and aerospace systems.
  • Development of cognitive engines for applications systems that can be implemented on future NASA missions and commercial products.