Cooperative spectrum sensing in TV white spaces: When cognitive radio meets cloud

Chun Hsien Ko*, Din Hwa Huang, Sau-Hsuan Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

A Cognitive Radio Cloud Network (CRCN) in TV White Spaces (TVWS) is proposed in this paper. Under the infrastructure of CRCN, cooperative spectrum sensing (SS) and resource scheduling in TVWS can be efficiently implemented making use of the scalability and the vast storage and computing capacity of the Cloud. Based on the sensing reports collected on the Cognitive Radio Cloud (CRC) from distributed secondary users (SUs), we study and implement a sparse Bayesian learning (SBL) algorithm for cooperative SS in TVWS using Microsoft's Windows Azure Cloud platform. A database for the estimated locations and spectrum power profiles of the primary users are established on CRC with Microsoft's SQL Azure. Moreover to enhance the performance of the SBL-based SS on CRC, a hierarchical parallelization method is also implemented with Microsoft's dotNet 4.0 in a MapReduce-like programming model. Based on our simulation studies, a proper programming model and partitioning of the sensing data play crucial roles to the performance of the SBL-based SS on the Cloud.

Original languageEnglish
Title of host publication2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011
Pages672-677
Number of pages6
DOIs
StatePublished - 26 Jul 2011
Event2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011 - Shanghai, China
Duration: 10 Apr 201115 Apr 2011

Publication series

Name2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011

Conference

Conference2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011
CountryChina
CityShanghai
Period10/04/1115/04/11

Keywords

  • Cloud Computing
  • Cognitive Radio
  • MapReduce
  • Sparse Bayesian Learning
  • Windows Azure

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