A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks (original) (raw)

8th International Conference on Mobile Multimedia Communications

Research Article

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@INPROCEEDINGS{10.4108/icst.mobimedia.2015.259026,
author={Zhu Jiang and Xiong Jiahao and Chen Hongcui and Han Chao},
title={A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks},
proceedings={8th International Conference on Mobile Multimedia Communications},
publisher={EAI},
proceedings_a={MOBIMEDIA},
year={2015},
month={8},
keywords={cognitive radio spectrum allocation quantum particle swarm multi-objective optimization},
doi={10.4108/icst.mobimedia.2015.259026}
}
Zhu Jiang
Xiong Jiahao
Chen Hongcui
Han Chao
Year: 2015
A Spectrum Allocation Mechanism Based on HJ-DQPSO for Cognitive Radio Networks
MOBIMEDIA
ICST
DOI: 10.4108/icst.mobimedia.2015.259026
1: Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications

Abstract

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.

Keywords

cognitive radio, spectrum allocation, quantum particle swarm, multi-objective optimization

Published

2015-08-03

Publisher

EAI

http://dx.doi.org/10.4108/icst.mobimedia.2015.259026