Sensing Statistical Primary Network Patterns via Bayesian Network Structure Learning

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users, as a large number of applications demand for more comprehensive knowledge on primary network behaviors in spatial, temporal, and frequency domains. To satisfy such requirements, the authors study the statistical relationship among primary nodes by introducing a Bayesian network (BN)-based framework. How to efficiently learn such a BN structure is a long-standing issue that is not fully understood even in the statistical learning community. To address such an issue in CR, this paper proposes a BN structure learning scheme consisting of a concise directional dependence checking function and a regular BN graph, which achieves significantly lower computational complexity compared with existing approaches. With this result, cognitive users could efficiently understand the statistical behavior patterns in the primary networks, such that more efficient cognitive protocols could be designed across different network layers.

• Order URL:
  https://doi.org/10.1109/TVT.2016.2593901
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • Han, Weijia
  • Sang, Huiyan
  • Ma, Xiao
  • Li, Jiandong
  • Zhang, Yanyan
  • Cui, Shuguang
• Publication Date: 2017-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References;
• Pagination: pp 3143-3157
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 66
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Machine learning; Sensors; Telecommunications; Wireless communication systems
• Uncontrolled Terms: Base stations; Bayesian networks; Cognitive radio networks
• Subject Areas: Data and Information Technology; Transportation (General);

Filing Info

• Accession Number: 01637687
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 27 2017 11:37AM