A conceptual model and prototype of cognitive radio cloud networks in TV white spaces

Sau-Hsuan Wu; Hsi-Lu Chao; Chung Ting Jiang; Shang Ru Mo; Chun Hsien Ko; Tzung Lin Li; Chiau Feng Liang; Chung Chieh Cheng

doi:10.1109/WCNCW.2012.6215536

A conceptual model and prototype of cognitive radio cloud networks in TV white spaces

Sau-Hsuan Wu^*, Hsi-Lu Chao, Chung Ting Jiang, Shang Ru Mo, Chun Hsien Ko, Tzung Lin Li, Chiau Feng Liang, Chung Chieh Cheng

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A Cognitive Radio Cloud Network (CRCN) model is proposed for wireless communications in TV White Spaces (TVWS). Making use of the flexible and vast computing capacity of the Cloud, a database and a sparse Bayesian learning (SBL) algorithm are developed for cooperative spectrum sensing (CSS) and implemented on Microsoft's Windows Azure Cloud platform. A medium access control (MAC) scheme is also prototyped for this CRCN model to collect sensing reports and access channels with Rice University's wireless access research platform (WARP). Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the time to detect the presence and/or the absence of primary users, and the channel vacating delay are measured and analyzed for the design and deployment of the future CRCN.

Original language	English
Title of host publication	2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012
Pages	425-430
Number of pages	6
DOIs	https://doi.org/10.1109/WCNCW.2012.6215536
State	Published - 16 Jul 2012
Event	2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012 - Paris, France Duration: 1 Apr 2012 → 1 Apr 2012

Publication series

Name	2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012

Conference

Conference	2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012
Country/Territory	France
City	Paris
Period	1/04/12 → 1/04/12

Keywords

CR-MAC
Cloud Computing
Cognitive Radio
Cooperative Spectrum Sensing and Sparse Bayesian Learning

Access to Document

10.1109/WCNCW.2012.6215536

Cite this

Wu, S.-H., Chao, H.-L., Jiang, C. T., Mo, S. R., Ko, C. H., Li, T. L., Liang, C. F., & Cheng, C. C. (2012). A conceptual model and prototype of cognitive radio cloud networks in TV white spaces. In 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012 (pp. 425-430). Article 6215536 (2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012). https://doi.org/10.1109/WCNCW.2012.6215536

@inproceedings{87cd7d6c011d40c1b774c8cb31143cb9,

title = "A conceptual model and prototype of cognitive radio cloud networks in TV white spaces",

abstract = "A Cognitive Radio Cloud Network (CRCN) model is proposed for wireless communications in TV White Spaces (TVWS). Making use of the flexible and vast computing capacity of the Cloud, a database and a sparse Bayesian learning (SBL) algorithm are developed for cooperative spectrum sensing (CSS) and implemented on Microsoft's Windows Azure Cloud platform. A medium access control (MAC) scheme is also prototyped for this CRCN model to collect sensing reports and access channels with Rice University's wireless access research platform (WARP). Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the time to detect the presence and/or the absence of primary users, and the channel vacating delay are measured and analyzed for the design and deployment of the future CRCN.",

keywords = "CR-MAC, Cloud Computing, Cognitive Radio, Cooperative Spectrum Sensing and Sparse Bayesian Learning",

author = "Sau-Hsuan Wu and Hsi-Lu Chao and Jiang, {Chung Ting} and Mo, {Shang Ru} and Ko, {Chun Hsien} and Li, {Tzung Lin} and Liang, {Chiau Feng} and Cheng, {Chung Chieh}",

year = "2012",

month = jul,

day = "16",

doi = "10.1109/WCNCW.2012.6215536",

language = "English",

isbn = "9781467306829",

series = "2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012",

pages = "425--430",

booktitle = "2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012",

note = "2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012 ; Conference date: 01-04-2012 Through 01-04-2012",

}

Wu, S-H , Chao, H-L, Jiang, CT, Mo, SR, Ko, CH, Li, TL, Liang, CF & Cheng, CC 2012, A conceptual model and prototype of cognitive radio cloud networks in TV white spaces. in 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012., 6215536, 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012, pp. 425-430, 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012, Paris, France, 1/04/12. https://doi.org/10.1109/WCNCW.2012.6215536

A conceptual model and prototype of cognitive radio cloud networks in TV white spaces. / Wu, Sau-Hsuan ; Chao, Hsi-Lu; Jiang, Chung Ting et al.
2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012. 2012. p. 425-430 6215536 (2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A conceptual model and prototype of cognitive radio cloud networks in TV white spaces

AU - Wu, Sau-Hsuan

AU - Chao, Hsi-Lu

AU - Jiang, Chung Ting

AU - Mo, Shang Ru

AU - Ko, Chun Hsien

AU - Li, Tzung Lin

AU - Liang, Chiau Feng

AU - Cheng, Chung Chieh

PY - 2012/7/16

Y1 - 2012/7/16

N2 - A Cognitive Radio Cloud Network (CRCN) model is proposed for wireless communications in TV White Spaces (TVWS). Making use of the flexible and vast computing capacity of the Cloud, a database and a sparse Bayesian learning (SBL) algorithm are developed for cooperative spectrum sensing (CSS) and implemented on Microsoft's Windows Azure Cloud platform. A medium access control (MAC) scheme is also prototyped for this CRCN model to collect sensing reports and access channels with Rice University's wireless access research platform (WARP). Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the time to detect the presence and/or the absence of primary users, and the channel vacating delay are measured and analyzed for the design and deployment of the future CRCN.

AB - A Cognitive Radio Cloud Network (CRCN) model is proposed for wireless communications in TV White Spaces (TVWS). Making use of the flexible and vast computing capacity of the Cloud, a database and a sparse Bayesian learning (SBL) algorithm are developed for cooperative spectrum sensing (CSS) and implemented on Microsoft's Windows Azure Cloud platform. A medium access control (MAC) scheme is also prototyped for this CRCN model to collect sensing reports and access channels with Rice University's wireless access research platform (WARP). Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the time to detect the presence and/or the absence of primary users, and the channel vacating delay are measured and analyzed for the design and deployment of the future CRCN.

KW - CR-MAC

KW - Cloud Computing

KW - Cognitive Radio

KW - Cooperative Spectrum Sensing and Sparse Bayesian Learning

UR - http://www.scopus.com/inward/record.url?scp=84863693706&partnerID=8YFLogxK

U2 - 10.1109/WCNCW.2012.6215536

DO - 10.1109/WCNCW.2012.6215536

M3 - Conference contribution

AN - SCOPUS:84863693706

SN - 9781467306829

T3 - 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012

SP - 425

EP - 430

BT - 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012

T2 - 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012

Y2 - 1 April 2012 through 1 April 2012

ER -

A conceptual model and prototype of cognitive radio cloud networks in TV white spaces

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this