2016 IEEE Symposium on Machine Learning for Characterization of Cognitive Communications and Radar (GlobalSIP 2016)

December 7- 9, 2016

Indonesia

Call For Papers

The 8th International Conference on Information Technology and Electrical Engineering (ICITEE) 2016 aims to strengthen the collaboration and provide a forum for academicians, professionals and researchers to discuss and exchange their research results, innovative ideas, and experiences in all aspects of intelligent and advanced technologies, as well as to identify emerging research topics and define the future directions to achieve sustainable development.

Topics

 Learning in partially observable RF environments
 Multi-agent learning in distributed cognitive radio networks
 Machine learning for cooperative spectrum sensing
 Autonomous learning in unknown RF environments
 Distributed learning techniques for cognitive radio networks
 Characterization of multi-dimensional activity dynamics of CRNs
 Machine learning of the topology and structural properties of CRNs
 Quality of learning with corrupted, censored and missing spectrum sensing samples
 Joint optimization and learning of spectrum usage dynamics and spectrum access control
 Challenges in machine learning for cognitive radars
 Privacy-preserving machine learning for cognitive radio
 Privacy-preserving machine learning for cognitive radar
 Machine learning for cognitive technologies in 5G cellular networks
 Cloud-based machine learning for cognitive communications and radar
 Non-parametric machine learning for cognitive radio and radar
 Generative Models for machine learning in cognitive communications and radar
 Machine learning for geolocation in cognitive communications and radar
 Adaptive information-centric cognitive networks
 Network estimation for cognitive networks

IMPORTANT DATES

Paper submission: June 5, 2016

                       

Notification of acceptance: August 5, 2016

           

Final manuscripts due: September 5, 2016

                       

User Name : MANI
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