Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks

Zhenghao ZHANG; Husheng LI; Changxing PEI; Qi ZENG

doi:10.1587/transcom.E94.B.3569

Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks

Zhenghao ZHANG, Husheng LI, Changxing PEI, Qi ZENG

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There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.

Publication: IEICE TRANSACTIONS on Communications Vol.E94-B No.12 pp.3569-3578

Publication Date: 2011/12/01

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Online ISSN: 1745-1345

DOI: 10.1587/transcom.E94.B.3569

Type of Manuscript: PAPER

Category: Terrestrial Wireless Communication/Broadcasting Technologies

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Zhenghao ZHANG, Husheng LI, Changxing PEI, Qi ZENG, "Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks" in IEICE TRANSACTIONS on Communications, vol. E94-B, no. 12, pp. 3569-3578, December 2011, doi: 10.1587/transcom.E94.B.3569.
Abstract: There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.3569/_p

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@ARTICLE{e94-b_12_3569,
author={Zhenghao ZHANG, Husheng LI, Changxing PEI, Qi ZENG, },
journal={IEICE TRANSACTIONS on Communications},
title={Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks},
year={2011},
volume={E94-B},
number={12},
pages={3569-3578},
abstract={There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.},
keywords={},
doi={10.1587/transcom.E94.B.3569},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3569
EP - 3578
AU - Zhenghao ZHANG
AU - Husheng LI
AU - Changxing PEI
AU - Qi ZENG
PY - 2011
DO - 10.1587/transcom.E94.B.3569
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2011
AB - There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.
ER -