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A Heterogeneous Network Access Selection Algorithm Based on Attribute Dependence

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Abstract

In order to solve the problem which fails to consider the degree of attribute dependence in current network access selection schemes, a novel heterogeneous network access selection scheme based on attribute dependence is proposed in this paper. The scheme translates the network access selection problem into the problem of multi-attribute decision making based on attribute dependence and solves it using the chaotic glowworm swarm based algorithm. First, the degree of attribute dependence is measured and the access selection model is established based on the degree of attribute dependence. Then, the chaotic glowworm swarm based algorithm is used to solve the optimal weight in the model. Finally, the user accesses the network with the best performance based on the access selection model. The simulation results demonstrate the improved performance of the proposed access selection scheme compared with other schemes. The proposed scheme can reduce blocking and handoff dropping rate, as well as the number of handoff. Moreover, the proposed scheme achieves the load balance of each network.

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Acknowledgments

The work is supported by the 863 program No. 2014AA01A701, the Beijing Natural Science Foundation (4142049) and the Fundamental Research Funds for the Central Universities of China No. 2015XS07. Thanks are due to Shimo Du for assistance with the experiments and complexity analysis.

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Authors and Affiliations

  1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing, 102206, China

    Liangrui Tang & Shiyu Ji

  2. School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing, 102206, China

    Jiangyu Yan

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  1. Liangrui Tang
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  2. Shiyu Ji
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  3. Jiangyu Yan
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Correspondence to Shiyu Ji.

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Tang, L., Ji, S. & Yan, J. A Heterogeneous Network Access Selection Algorithm Based on Attribute Dependence. Wireless Pers Commun 92, 1163–1176 (2017). https://doi.org/10.1007/s11277-016-3600-6

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  • DOI: https://doi.org/10.1007/s11277-016-3600-6

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