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Optimizing Power and Rate in Cognitive Radio Networks using Improved Particle Swarm Optimization with Mutation Strategy

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Abstract

Dynamic spectrum allocation is a main challenge in the design of cognitive radio networks, which enables wireless devices to opportunistically access portions of the spectrum as they become available. Considering this challenge, this paper proposes a nonconvex power and rate management algorithm in cognitive radio networks. We apply an improved particle swarm optimization (PSO) method to deal with this nonconvexity issue directly without any assumption, which is different from prior works. Since PSO sometimes converges around the local optimum solution in the early stage of the searching process, mutation is employed to PSO which can speed up convergence and escape local optimum. We also give the numerical results, which show that the proposed algorithm can achieve higher quality solutions than other population-based optimization techniques.

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Acknowledgments

This work is supported by National Science Foundation of China Grants 61174097, 61172064, 61374108, Science Foundation of Shandong 2015ZRB01121.

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Authors and Affiliations

  1. Institute of Mathematics and Statistics, Ludong University, Yantai, 264025, People’s Republic of China

    Meiqin Tang, Yalin Xin, Xinjiang Wei & Xiaohua Liu

  2. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Chengnian Long

Authors
  1. Meiqin Tang
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  2. Yalin Xin
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  3. Chengnian Long
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  4. Xinjiang Wei
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  5. Xiaohua Liu
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Correspondence to Meiqin Tang.

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Tang, M., Xin, Y., Long, C. et al. Optimizing Power and Rate in Cognitive Radio Networks using Improved Particle Swarm Optimization with Mutation Strategy. Wireless Pers Commun 89, 1027–1043 (2016). https://doi.org/10.1007/s11277-016-3303-z

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