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A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database

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Abstract

The architecture of cognitive decision engine should enable fast decision making, long-term knowledge accumulating based on past operating experience, and capabilities of knowledge updating to adapt to new situations. In this paper, a hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database is proposed. Considering the user’s quality of service preferences and the wireless situations, how to determine the radio’s link parameters such as modulation type, symbol rate, and transmit power can be formulated as a multi-objective optimization problem. In the architecture, this problem is solved by using particle swarm optimization algorithms, which make cognitive radio have the fast decision-making ability when facing unknown wireless situations. The case database, which stores the past running experiences of the cognitive radio is also integrated into the proposed architecture to improve the radio’s response speed and endows the radio with the ability of learning from its previous operating experiences. Simulation results show the effectiveness of the architecture, and the proposed cognitive decision engine can dynamically and properly reconfigure the radio according to the changes in wireless environment.

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Acknowledgments

Our team is undertaking a project named “Basic Theory and Key Technologies in Cognitive Wireless Networks,” which is one of the major projects of the National Basic Research Program. This work was supported by the National Basic Research Program (973) of China under grant no. 2009CB3020400 and the National Science Fund of China under grant no. 61001106. Also, the authors would like to thank Aijing Li, Yuzheng Huang, and Liu Yang for their careful reading and helpful suggestions, which have contributed in improving the quality of our present paper.

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Authors and Affiliations

  1. Institute of Communication Engineering, PLA University of Science and Technology, Nanjing, China

    Xiaobo Tan & Hang Zhang

  2. Durham University, Durham, UK

    Jian Hu

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  1. Xiaobo Tan
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  2. Hang Zhang
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Correspondence to Xiaobo Tan.

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Tan, X., Zhang, H. & Hu, J. A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database. Ann. Telecommun. 69, 593–605 (2014). https://doi.org/10.1007/s12243-013-0417-0

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  • DOI: https://doi.org/10.1007/s12243-013-0417-0

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