Spatial Diversity Aware Data Fusion for Cooperative Spectrum Sensing

Nuno Kiilerich Pratas, Neeli R. Prasad, António Rodrigues, Ramjee Prasad

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2 Citationer (Scopus)
109 Downloads (Pure)

Resumé

Studies have shown that when data fusion schemes are used in cooperative spectrum sensing, there is a significant gap between the available resources and the ones perceived by the network.

In this paper a cluster based adaptive counting rule is proposed, where the local detectors that experience similar signal conditions are grouped by the fusion center in clusters and where the data fusion is then done separately at each cluster.

The proposed algorithm uses the correlation between the binary decisions of the local detectors over an observation window to select the cluster where each local detector should go. It was observed that in the case where there is only one signal source, that the proposed algorithm is able to achieve the same level of performance when compared to the perfect clustering algorithm where full information about the signal conditions at each local detector is available.
OriginalsprogEngelsk
TidsskriftEuropean Signal Processing Conference (EUSIPCO)
Sider (fra-til)2669-2673
Antal sider5
ISSN2076-1465
StatusUdgivet - 2012
Begivenhed20th European Signal Processing Conference - Bucharest, Rumænien
Varighed: 27 aug. 201231 aug. 2012

Konference

Konference20th European Signal Processing Conference
LandRumænien
ByBucharest
Periode27/08/201231/08/2012

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Data fusion
Detectors
Clustering algorithms

Citer dette

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title = "Spatial Diversity Aware Data Fusion for Cooperative Spectrum Sensing",
abstract = "Studies have shown that when data fusion schemes are used in cooperative spectrum sensing, there is a significant gap between the available resources and the ones perceived by the network.In this paper a cluster based adaptive counting rule is proposed, where the local detectors that experience similar signal conditions are grouped by the fusion center in clusters and where the data fusion is then done separately at each cluster.The proposed algorithm uses the correlation between the binary decisions of the local detectors over an observation window to select the cluster where each local detector should go. It was observed that in the case where there is only one signal source, that the proposed algorithm is able to achieve the same level of performance when compared to the perfect clustering algorithm where full information about the signal conditions at each local detector is available.",
author = "{Kiilerich Pratas}, Nuno and Prasad, {Neeli R.} and Ant{\'o}nio Rodrigues and Ramjee Prasad",
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Spatial Diversity Aware Data Fusion for Cooperative Spectrum Sensing. / Kiilerich Pratas, Nuno; Prasad, Neeli R.; Rodrigues, António; Prasad, Ramjee.

I: European Signal Processing Conference (EUSIPCO), 2012, s. 2669-2673.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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AU - Rodrigues, António

AU - Prasad, Ramjee

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AB - Studies have shown that when data fusion schemes are used in cooperative spectrum sensing, there is a significant gap between the available resources and the ones perceived by the network.In this paper a cluster based adaptive counting rule is proposed, where the local detectors that experience similar signal conditions are grouped by the fusion center in clusters and where the data fusion is then done separately at each cluster.The proposed algorithm uses the correlation between the binary decisions of the local detectors over an observation window to select the cluster where each local detector should go. It was observed that in the case where there is only one signal source, that the proposed algorithm is able to achieve the same level of performance when compared to the perfect clustering algorithm where full information about the signal conditions at each local detector is available.

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