IEICE TRANSACTIONS on Communications
Optimization of Cooperative Spectrum Sensing in Cluster-Based Cognitive Radio Networks with Soft Data Fusion
Summary :
This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E96-B No.11 pp.2923-2932
- Publication Date
- 2013/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E96.B.2923
- Type of Manuscript
- PAPER
- Category
- Terrestrial Wireless Communication/Broadcasting Technologies
Authors
Ying WANG
Beijing University of Posts and Telecommunications
Wenxuan LIN
Beijing University of Posts and Telecommunications
Weiheng NI
Beijing University of Posts and Telecommunications
Ping ZHANG
Beijing University of Posts and Telecommunications
Keyword
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Ying WANG, Wenxuan LIN, Weiheng NI, Ping ZHANG, "Optimization of Cooperative Spectrum Sensing in Cluster-Based Cognitive Radio Networks with Soft Data Fusion" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 11, pp. 2923-2932, November 2013, doi: 10.1587/transcom.E96.B.2923.
Abstract: This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.2923/_p
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@ARTICLE{e96-b_11_2923,
author={Ying WANG, Wenxuan LIN, Weiheng NI, Ping ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimization of Cooperative Spectrum Sensing in Cluster-Based Cognitive Radio Networks with Soft Data Fusion},
year={2013},
volume={E96-B},
number={11},
pages={2923-2932},
abstract={This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.},
keywords={},
doi={10.1587/transcom.E96.B.2923},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Optimization of Cooperative Spectrum Sensing in Cluster-Based Cognitive Radio Networks with Soft Data Fusion
T2 - IEICE TRANSACTIONS on Communications
SP - 2923
EP - 2932
AU - Ying WANG
AU - Wenxuan LIN
AU - Weiheng NI
AU - Ping ZHANG
PY - 2013
DO - 10.1587/transcom.E96.B.2923
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2013
AB - This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.
ER -
English
