H. Ding, Y. Fang, X. Huang, M. Pan, P. Li and S. Glisic, "Cognitive Capacity Harvesting Networks: Architectural Evolution Toward Future Cognitive Radio Networks," in IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1902-1923, thirdquarter 2017. doi: 10.1109/COMST.2017.2677082
Cognitive capacity harvesting networks : architectural evolution toward future cognitive radio networks
|Author:||Ding, Haichuan1; Fang, Yuguang1,2; Huang, Xiaoxia3;|
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611 USA
2Dalian Maritime University, Dalian, China
3 ShenzheInstitutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
4Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204 USA
5Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA
6Telecommunications Laboratory, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018073133183
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-07-31
Cognitive radio technologies enable users to opportunistically access unused licensed spectrum and are viewed as a promising way to deal with the current spectrum crisis. Over the last 15 years, cognitive radio technologies have been extensively studied from algorithmic design to practical implementation. One pressing and fundamental problem is how to integrate cognitive radios into current wireless networks to enhance network capacity and improve users' experience. Unfortunately, existing solutions to cognitive radio networks (CRNs) suffer from many practical design issues. To foster further research activities in this direction, we attempt to provide a tutorial for CRN architecture design. Noticing that an effective architecture for CRNs is still lacking, in this tutorial, we systematically summarize the principles for CRN architecture design and present a novel flexible network architecture, termed cognitive capacity harvesting network (CCHN), to elaborate on how a CRN architecture can be designed. Unlike existing architectures, we introduce a new network entity, called secondary service provider, and deploy cognitive radio capability enabled routers, called cognitive radio routers, in order to effectively and efficiently manage resource harvesting and mobile traffic while enabling users without cognitive radios to access and enjoy CCHN services. Our analysis shows that our CCHN aligns well to industrial standardization activities and hence provides a viable approach to implementing future CRNs. We hope that our proposed design approach opens a new venue to future CRN research.
IEEE communications surveys and tutorials
|Pages:||1902 - 1923|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the U.S. National Science Foundation under Grant CNS-1343356, Grant CNS-1409797, and Grant CNS-1423165. The work of X. Huang was supported in part by the NSFC-Guangdong Joint Program under Grant U1501255 and Grant U1301256, in part by the Guangdong Science and Technology Project under Grant 2015A010103009, and in part by the Shenzhen Science and Technology Project under Grant CXZZ20150401152251212. The work of M. Pan was supported in part by the U.S. National Science Foundation under Grant CNS-1343361, Grant CNS-1350230, and Grant CPS-1646607. The work of P. Li was supported by the U.S. National Science Foundation under Grant CNS-1602172 and Grant CNS-1566479. The work of S. Glisic was supported by the Taseen käitto CWC-NS-Glisic-Menot under Project 240007101.
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