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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chun-Hsien Ko Din Hwa Huang Sau-Hsuan Wu |
| Copyright Year | 2011 |
| Description | Author affiliation: Institute of Communications Engineering, National Chiao Tung University, Hsinchu, Taiwan 300 (Chun-Hsien Ko; Din Hwa Huang; Sau-Hsuan Wu) |
| 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. |
| Starting Page | 672 |
| Ending Page | 677 |
| File Size | 549435 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457702495 |
| e-ISBN | 9781457702488 |
| DOI | 10.1109/INFCOMW.2011.5928897 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-04-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | TV Databases Cognitive Radio Cloud Computing Computational modeling FCC Sparse Bayesian Learning Windows Azure Programming Sensors Cognitive radio MapReduce |
| Content Type | Text |
| Resource Type | Article |
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