基于疯狂自适应鱼群算法的认知无线电频谱分配

doi:10.3969/j.issn.0255-8297.2020.06.005

摘要/Abstract

摘要： 针对目前认知无线电网络中频谱利用无法满足指数级增长的通信需求、人工鱼群算法保持种群多样性差、全局搜索能力弱的问题，对图论频谱分配模型的人工鱼群算法进行了改进，得到了网络效益函数最优情况下的频谱分配.首先自适应调整视野和步长，保证算法前期较强的全局搜索能力和后期的收敛精度；然后在随机行为模式下引入疯狂算子，产生扰动以增加种群多样性.仿真实验对比了4种不同算法在相同模型参数下的系统总效益，同时对可用频谱和认知用户分别设置控制变量法测试算法性能.结果表明：改进后的人工鱼群算法全局搜索能力较强，具有较强的鲁棒性.

关键词: 认知无线电网络, 频谱分配, 人工鱼群算法, 疯狂算子, 种群多样性

Abstract: Facing the facts in cognitive radio networks that spectrum utilization cannot meet the demand of exponentially increasing communication, and artificial fish swarm algorithm maintains poor population diversity and weak global search ability, this paper propose an improved artificial fish swarm algorithm under the graph theory spectrum allocation model to obtain the optimal spectrum allocation under the network benefit function. Firstly, the field of visual and step size are adjusted adaptively to ensure the algorithm having strong global search ability of the algorithm in earlier stages and high convergence accuracy in later stages. Secondly, crazy operators are introduced into the food source to generate disturbances with increased population diversity. By comparing the total benefits of four algorithms in the same spectrum environment, and setting up control variable method separately for the available spectrum and cognitive users, we test the performance of the algorithm. Simulation experiment indicates that the improved artificial fish swarm algorithm has better global search capability and robustness compared with other three algorithms.

Key words: cognitive radio network, spectrum allocation, artificial fish swarm algorithm, crazy operator, population diversity

中图分类号:

TN98

苏慧慧, 彭艺, 曲文博. 基于疯狂自适应鱼群算法的认知无线电频谱分配[J]. 应用科学学报, 2020, 38(6): 882-889.

SU Huihui, PENG Yi, QU Wenbo. Cognitive Radio Spectrum Allocation Based on Crazy Adaptive Fish Swarm Algorithm[J]. Journal of Applied Sciences, 2020, 38(6): 882-889.

参考文献

[1] 徐兴坤. 认知无线网络中资源优化问题的研究[D]. 北京:北京邮电大学, 2010.
[2] 朱冰莲, 朱文勇, 朱志青, 等. 基于统计特性的蝙蝠算法在认知无线电频谱分配中的应用[J]. 系统工程与电子技术, 2018, 40(2):441-446. Zhu B L, Zhu W Y, Zhu Z Q, et al. Application of bat algorithm based on statistical characteristics in spectrum allocation of cognitive radio[J]. System Engineering and Electronics, 2018, 40(2):441-446. (in Chinese)
[3] 毕晓明, 邹小红. 探索精细化电磁环境测绘能力建设提升频谱资源管理科学化工作水平[J]. 中国无线电, 2016, 41(12):59-61. Bi X M, Zou X H. Exploring the construction of refined electromagnetic environment surveying and mapping capabilities to improve the scientific level of spectrum resource management[J]. China Radio, 2016, 41(12):59-61. (in Chinese)
[4] 李轩衡. 认知无线网络中频谱共享方法研究[D]. 大连:大连理工大学, 2017.
[5] Peng C Y, Zheng H T, Zhao B Y. Utilization and fairness in spectrum assignment for opportunistic spectrum access[J]. Mobile Networks and Applications, 2006, 11(4):555-576.
[6] Karaca H M, Kurt T, Dicle S Z, et al. Auction-based throughput maximization in cognitive radio networks under interference constraint[J]. Wireless Personal Communications, 2013, 72(2):2391-2395.
[7] 谢玉鹏, 谭学治, 马琳, 等. 基于分布式博弈论的频谱分配算法[J]. 系统工程与电子技术, 2015, 37(10):2391-2395. Xie Y P, Tan X Z, Ma L, et al. Spectrum allocation algorithm based on distributed game theory[J]. System Engineering and Electronics, 2015, 37(10):2391-2395. (in Chinese)
[8] Zhao Z J, Peng Z, Zheng S L. Cognitive radio spectrum allocation using evolutionary algorithms[J]. IEEE Transactions on Wireless Communications, 2009, 8(9):4421-4425.
[9] 赵知劲, 彭振, 郑仕链, 等. 基于量子遗传算法的认知无线电频谱分配[J]. 物理学报, 2009, 58(2):1358-1363. Zhao Z J, Peng Z, Zheng S L, et al. Cognitive radio spectrum allocation based on quantum genetic algorithm[J]. Acta Physica Sinica 2009, 58(2):1358-1363. (in Chinese)
[10] 滕志军, 谢露莹, 滕利鑫, 等. 基于混沌二进制粒子群算法的认知无线电频谱分配策略[J]. 重庆邮电大学学报(自然科学版), 2019(5):601-608. Teng Z J, Xie L Y, Teng L X, et al. Cognitive radio spectrum allocation strategy based on chaos binary particle swarm optimization algorithm[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2019(5):601-608. (in Chinese)
[11] 柴争义, 刘芳. 基于免疫克隆选择优化的认知无线网络频谱分配[J]. 通信学报, 2010, 31(11):92-100. Chai Z Y, Liu F. Spectrum allocation of cognitive wireless networks based on immune clone selection optimization[J]. Journal of Communications, 2010, 31(11):92-100. (in Chinese)
[12] 高洪元, 曹金龙. 量子蜂群算法及其在认知频谱分配中的应用[J]. 中南大学学报(自然科学版), 2012, 43(12):4743-4749. Gao H Y, Cao J L. Quantum bee colony algorithm and its application in cognitive spectrum allocation[J]. Journal of Central South University (Science and Technology Edition), 2012, 43(12):4743-4749. (in Chinese)
[13] 刘葳汉. 基于遗传和人工鱼群融合算法的频谱分配优化[D]. 长春:吉林大学, 2017.
[14] 王联国. 人工鱼群算法及其应用研究[D]. 兰州:兰州理工大学, 2009.
[15] 徐建波, 戴月明, 严大虎. 双自适应人工鱼群优化算法[J]. 微电子学与计算机, 2018, 35(4):53-57. Xu J B, Dai Y M, Yan D H. Double adaptive artificial fish school optimization algorithm[J]. Microelectronics & Computer, 2018, 35(4):53-57. (in Chinese)
[16] 朱旭辉, 倪志伟, 程美英. 变步长自适应的改进人工鱼群算法[J]. 计算机科学, 2015, 42(2):210-216, 246. Zhu X H, Ni Z W, Cheng M Y. Improved artificial fish school algorithm with variable step size adaptation[J]. Computer Science, 2015, 42(2):210-216, 246. (in Chinese)
[17] Taranjit K, Barjinder S S, Savita G. An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm[J]. Multimedia Tools and Applications, 2019, 78(15):21853-21890.
[18] 孙海建. 基于粒子群算法和遗传算法的频谱分配研究[D]. 吉林:吉林大学, 2015.