Hierarchical cognition cycle for cognitive radio networks

Cognitive radio (CR) can bring about remarkable improvement in spectrum utilization. Different cognition cycles have been proposed in recent years. However, most of the existing works only emphasize functional or operational aspects of cognition cycle, regardless of other indispensable aspects and the connection between them. To deal with the emerging situation of “data rich, information vague, knowledge poor” in cognitive radio networks (CRNs), we propose the hierarchical cognition cycle (HCC) as a new transdisciplinary research field in this paper. HCC investigates a fundamental problem, which is how to manage available resources in the complex environment to meet various demands in CRN. A comprehensive theoretical framework of HCC is established in terms of the core, the essence loop, the function loop, the operation loop, and the external loop of HCC. The reduction of uncertainty in CRN is studied and several new metrics in HCC are defined. Furthermore, a few research challenges ahead are presented as well.

[1]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[2]  Yingxu Wang,et al.  Special Issue on Cybernetics and Cognitive Informatics , 2009, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Yang Liu,et al.  Intelligent and efficient development of wireless networks: A review of cognitive radio networks , 2012 .

[4]  Dennis Gabor,et al.  Theory of communication , 1946 .

[5]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[6]  Masahiro Umehira,et al.  Wide area ubiquitous network: the network operator's view of a sensor network , 2008, IEEE Communications Magazine.

[7]  Keping Long,et al.  Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey , 2013, IEEE Wireless Communications.

[8]  Keping Long,et al.  On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches , 2014, IEEE Communications Surveys & Tutorials.

[9]  Serafín Moral,et al.  Maximum of Entropy for Credal Sets , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Michael S. Hsiao,et al.  Cognitive Radio and Networking Research at Virginia Tech , 2009, Proceedings of the IEEE.

[11]  Qiaoyan Wen,et al.  Information-theoretic measures associated with rough set approximations , 2011, Inf. Sci..

[12]  Jacques Pinaton,et al.  A survey of semantic web standards to representing knowledge in problem solving situations , 2012, 2012 International Conference on Information Retrieval & Knowledge Management.

[13]  Qihui Wu,et al.  Multi-Domain Cognition for Cognitive Wireless Networks , 2011 .