Parameters optimization of cognitive network based on artificial physics multi-objective algorithm

被引：0
|
作者：
Chai, Zheng-Yi [1 ,3 ]
Wang, Bing [2 ]
Li, Ya-Lun [1 ]
Zhu, Si-Feng [4 ]
Wang, Ying-Feng [5 ]
机构：
[1] School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin,300384, China
[2] Department of Maritime, Henan Vocational and Technical College of Communications, Zhengzhou,Henan,450005, China
[3] Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing,100876, China
[4] School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou,Henan,466001, China
[5] College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou,Henan,450000, China
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 08期
关键词：
Pareto principle - Parameter estimation - Cognitive radio - Engines - Hamming distance;
D O I：
10.3969/j.issn.0372-2112.2015.08.009
中图分类号：
学科分类号：
摘要：
To solve the engine parameter adjustment problem of cognitive radio networks, an artificial physics multi-objective optimization algorithm was proposed. According to its binary encoded features of cognitive parameters, Hamming distance based individual ranking method was designed and particle updated equation was improved, and finally the Pareto optimal set were achieved. Simulation results show that under the multi-carrier environment, the proposed algorithm can adjust transmission power and modulation mode according to the changing of channel and cognitive user demands. So it meets the demands for parameters optimization. ©, 2015, Chinese Institute of Electronics. All right reserved.
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收藏
页码：1526 / 1530
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