A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks

In cognitive radio network model consisting of secondary users and primary users, in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users’ fairness to access network, this paper proposes a new discrete multi-objective combinatorial opti...

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Main Authors: Zhu Jiang, Xiong Jiahao, Chen Hongcui, Han Chao
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2015-11-01
Series:EAI Endorsed Transactions on Cognitive Communications
Subjects:
Online Access:http://eudl.eu/doi/10.4108/icst.mobimedia.2015.259026
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spelling doaj-718944dbb5524a74af797c56cfc2e1342020-11-25T01:35:06ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Cognitive Communications2313-45342015-11-01141410.4108/icst.mobimedia.2015.259026A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio NetworksZhu Jiang0Xiong Jiahao1Chen Hongcui2Han Chao3Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and TelecommunicationsChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications; 602165121@qq.comChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and TelecommunicationsChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and TelecommunicationsIn cognitive radio network model consisting of secondary users and primary users, in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users’ fairness to access network, this paper proposes a new discrete multi-objective combinatorial optimization mechanism—HJ-DQPSO based on Hooke Jeeves (HJ) and Quantum Particle Swarm Optimization (QPSO) algorithm. The mechanism adopts HJ algorithm to local search to prevent falling into the local optimum, and proposes a discrete QPSO algorithm to match the discrete spectrum assignment model. The mechanism has the advantages of approximating optimal solution, rapid convergence, less parameters, avoiding falling into local optimum. Compared with existing spectrum assignment algorithms, the simulation results show that according to different optimization objectives, the HJ-DQPSO optimization mechanism for multi-objective optimization can better approximate optimal solution and converge fast. We can obtain a reasonable spectrum allocation scheme in the case of satisfying multiple optimization objectives.http://eudl.eu/doi/10.4108/icst.mobimedia.2015.259026cognitive radiospectrum allocationquantum particle swarmmulti-objective optimization
collection DOAJ
language English
format Article
sources DOAJ
author Zhu Jiang
Xiong Jiahao
Chen Hongcui
Han Chao
spellingShingle Zhu Jiang
Xiong Jiahao
Chen Hongcui
Han Chao
A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
EAI Endorsed Transactions on Cognitive Communications
cognitive radio
spectrum allocation
quantum particle swarm
multi-objective optimization
author_facet Zhu Jiang
Xiong Jiahao
Chen Hongcui
Han Chao
author_sort Zhu Jiang
title A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
title_short A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
title_full A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
title_fullStr A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
title_full_unstemmed A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
title_sort spectrum allocation mechanism based on hj-dqpso for cognitive radio networks
publisher European Alliance for Innovation (EAI)
series EAI Endorsed Transactions on Cognitive Communications
issn 2313-4534
publishDate 2015-11-01
description In cognitive radio network model consisting of secondary users and primary users, in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users’ fairness to access network, this paper proposes a new discrete multi-objective combinatorial optimization mechanism—HJ-DQPSO based on Hooke Jeeves (HJ) and Quantum Particle Swarm Optimization (QPSO) algorithm. The mechanism adopts HJ algorithm to local search to prevent falling into the local optimum, and proposes a discrete QPSO algorithm to match the discrete spectrum assignment model. The mechanism has the advantages of approximating optimal solution, rapid convergence, less parameters, avoiding falling into local optimum. Compared with existing spectrum assignment algorithms, the simulation results show that according to different optimization objectives, the HJ-DQPSO optimization mechanism for multi-objective optimization can better approximate optimal solution and converge fast. We can obtain a reasonable spectrum allocation scheme in the case of satisfying multiple optimization objectives.
topic cognitive radio
spectrum allocation
quantum particle swarm
multi-objective optimization
url http://eudl.eu/doi/10.4108/icst.mobimedia.2015.259026
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