Distributed boundary estimation for spectrum sensing in cognitive radio networks

In a cognitive radio network, a primary user (PU) shares its spectrum with secondary users (SUs) temporally and spatially, while allowing for some interference. We consider the problem of estimating the no-talk region of the PU, i.e., the region outside which SUs may utilize the PU's spectrum r...

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Main Authors: Zhang, Yi, Tay, Wee Peng, Li, Kwok Hung, Gaïti, Dominique
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2015
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Online Access:https://hdl.handle.net/10356/81706
http://hdl.handle.net/10220/25255
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-817062020-03-07T13:57:21Z Distributed boundary estimation for spectrum sensing in cognitive radio networks Zhang, Yi Tay, Wee Peng Li, Kwok Hung Gaïti, Dominique School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems In a cognitive radio network, a primary user (PU) shares its spectrum with secondary users (SUs) temporally and spatially, while allowing for some interference. We consider the problem of estimating the no-talk region of the PU, i.e., the region outside which SUs may utilize the PU's spectrum regardless of whether the PU is transmitting or not. We propose a distributed boundary estimation algorithm that allows SUs to estimate the boundary of the no-talk region collaboratively through message passing between SUs, and analyze the trade-offs between estimation error, communication cost, setup complexity, throughput and robustness. Simulations suggest that our proposed scheme has better estimation performance and communication cost trade-off compared to several other alternative benchmark methods, and is more robust to SU sensing errors, except when compared to the least squares support vector machine approach, which however incurs a much higher communication cost. Accepted version 2015-03-23T07:31:29Z 2019-12-06T14:36:33Z 2015-03-23T07:31:29Z 2019-12-06T14:36:33Z 2014 2014 Journal Article Zhang, Y., Tay, W. P., Li, K. H., & Gaïti, D. (2014). Distributed boundary estimation for spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 32(11), 1961-1973. https://hdl.handle.net/10356/81706 http://hdl.handle.net/10220/25255 10.1109/JSAC.2014.1411RP08 en IEEE Journal on Selected Areas in Communications © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/JSAC.2014.1411RP08]. 46 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
Zhang, Yi
Tay, Wee Peng
Li, Kwok Hung
Gaïti, Dominique
Distributed boundary estimation for spectrum sensing in cognitive radio networks
description In a cognitive radio network, a primary user (PU) shares its spectrum with secondary users (SUs) temporally and spatially, while allowing for some interference. We consider the problem of estimating the no-talk region of the PU, i.e., the region outside which SUs may utilize the PU's spectrum regardless of whether the PU is transmitting or not. We propose a distributed boundary estimation algorithm that allows SUs to estimate the boundary of the no-talk region collaboratively through message passing between SUs, and analyze the trade-offs between estimation error, communication cost, setup complexity, throughput and robustness. Simulations suggest that our proposed scheme has better estimation performance and communication cost trade-off compared to several other alternative benchmark methods, and is more robust to SU sensing errors, except when compared to the least squares support vector machine approach, which however incurs a much higher communication cost.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Yi
Tay, Wee Peng
Li, Kwok Hung
Gaïti, Dominique
format Article
author Zhang, Yi
Tay, Wee Peng
Li, Kwok Hung
Gaïti, Dominique
author_sort Zhang, Yi
title Distributed boundary estimation for spectrum sensing in cognitive radio networks
title_short Distributed boundary estimation for spectrum sensing in cognitive radio networks
title_full Distributed boundary estimation for spectrum sensing in cognitive radio networks
title_fullStr Distributed boundary estimation for spectrum sensing in cognitive radio networks
title_full_unstemmed Distributed boundary estimation for spectrum sensing in cognitive radio networks
title_sort distributed boundary estimation for spectrum sensing in cognitive radio networks
publishDate 2015
url https://hdl.handle.net/10356/81706
http://hdl.handle.net/10220/25255
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