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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European Alliance for Innovation (EAI)
2015-11-01
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Online Access: | http://eudl.eu/doi/10.4108/icst.mobimedia.2015.259026 |
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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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