cogcom 15(4): e2

Research Article

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

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  • @ARTICLE{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},
        journal={EAI Endorsed Transactions on Cognitive Communications},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={COGCOM},
        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
    COGCOM
    EAI
    DOI: 10.4108/icst.mobimedia.2015.259026
Zhu Jiang1, Xiong Jiahao1,*, Chen Hongcui1, Han Chao1
  • 1: Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications
*Contact email: 602165121@qq.com

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.