SIG on Data-Driven Cognitive Networks
Scope and Objectives
In the last two decades, cognitive radio has emerged as an efficient way of improving the performance of communications systems, enhancing spectrum efficiency, and providing more flexibility in network management. A significant change in cognitive radio network (CRN) recently is that much more data are collected from various sources, including radio channels, user locations, service access data, social networking data, network-status and management data. The availability of these large amount of various types of data can potentially contribute to a revolution in CRN from a traditional knowledge-driven CRN into a more powerful data-driven CRN, which learning algorithm can drive to optimize its performance from the holistic aspects of signal processing (e.g., time series analysis), network planning, and user customization. In this SIG group, we provide a platform on the development of DDCN, including key technologies, data sharing opportunities and future research directions.
External LinkedIn Group web address
Li-Chun Wang, National Chiao Tung University, Taiwan
Yong Li, Tsinghua University, China
Steve Uhlig, Queen Mary, University of London, UK