认知无线电网络中基于免疫算法的多目标频谱分配

doi:10.16180/j.cnki.issn1007-7820.2019.04.012

电子科技 ›› 2019, Vol. 32 ›› Issue (4): 54-59.doi: 10.16180/j.cnki.issn1007-7820.2019.04.012

认知无线电网络中基于免疫算法的多目标频谱分配

高阳,韩韧,王清雲

上海理工大学光电信息与计算机工程学院,上海 200093

收稿日期:2018-03-18 出版日期:2019-04-15 发布日期:2019-03-27
作者简介:高阳(1991-),男,硕士研究生。研究方向:认知无线电技术等。|韩韧(1980-),男,博士,讲师。研究方向:无线通信网络等。|王清雲(1991-),男,硕士研究生。研究方向:云计算等。
基金资助:
上海市科技创新行动计划(17511109100)

Spectrum Allocation with Multi-Objective Optimization based on Immune Algorithm in Cognitive Radio Networks

GAO Yang,HAN Ren,WANG Qingyun

School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China

Received:2018-03-18 Online:2019-04-15 Published:2019-03-27
Supported by:
Action Plan for Technological Innovation of Shanghai(17511109100)

摘要/Abstract

摘要：

认知无线电网络中工作于同一信道的次用户越多,对网络造成的干扰越严重,因此如何在增加网络可服务次用户数量的同时提高网络吞吐量成为近年来的研究重点。针对此问题,文中提出了一种改进的多目标免疫算法。该算法将频谱效率和网络吞吐量作为求解目标,同时设计了适合问题求解的抗体编码方式。最后在获得适合该问题的Pareto最优解之后,与NSGA-II算法进行实验对比。仿真结果表明,所提方法在搜索Pareto最优解时性能更好,并且能够满足不同网络需求下的频谱分配要求。

关键词: 认知无线电, 频谱分配, 多目标优化, 频谱效率, 网络吞吐量, 免疫算法

Abstract:

Too many secondary users working on the same channel in a cognitive radio network will lead to serious interference to the network. Therefore, how to increase the network throughput while increasing the number of serviceable users in the network has become a research focus in recent years. An improved multi-objective immune algorithm was proposed to solve the problem which was addressed with respect to both spectrum efficiency and network throughput. Meanwhile, an antibody encoding that was suitable for solving the problem was designed. After obtaining the Pareto optimal solutions, the contrast test was conducted with the NSGA-II. The simulation results validated that the proposed strategy performed better in searching pareto optimal solutions and satisfied the network requirements of spectrum allocation in various cases.

Key words: cognitive radio, spectrum allocation, multi objective optimization, spectrum efficiency, network throughput, immune algorithm

中图分类号:

TN915

高阳,韩韧,王清雲. 认知无线电网络中基于免疫算法的多目标频谱分配[J]. 电子科技, 2019, 32(4): 54-59.

GAO Yang,HAN Ren,WANG Qingyun. Spectrum Allocation with Multi-Objective Optimization based on Immune Algorithm in Cognitive Radio Networks[J]. Electronic Science and Technology, 2019, 32(4): 54-59.

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参考文献 16

[1]	Aguilar-Gonzalez R, Cardenas-Juarez M, Pineda-Rico U , et al. Spectrum occupancy measurements below 1 GHz [C]. San Luis Potosi:Vehicular Technology Conference, 2014.
[2]	Tang J, Hincapie R, Xue G L , et al. Fair bandwidth allocation in wireless mesh networks with cognitive radios[J]. IEEE Transactions on Vehicular Technology, 2015,59(3):1487-1496. doi: 10.1109/TVT.2009.2038478
[3]	夏巧桥, 田茂, 汪鼎文 , 等. 基于免疫克隆算法的认知无线电多信道联合感知方法[J]. 电子与信息学报, 2014,36(1):55-60. doi: 10.3724/SP.J.1146.2013.00340
	Xia Qiaoqiao, Tian Mao, Wang Dingwen , et al. Immune clone algorithm based multiband joint detection method for cognitive radio[J]. Journal of Electronics and Information Technology, 2014,36(1):55-60. doi: 10.3724/SP.J.1146.2013.00340
[4]	Kuang Z F, Chen Z G . A effective multi-objective optimization spectrum allocation algorithm in cognitive wireless mesh networks[J]. Journal of Central South University, 2013,44(6):2346-2353.
[5]	尤晓建, 韩雪梅 . 基于鸡群算法的认知无线电决策引擎[J]. 软件导刊, 2017,16(7):128-130. doi: 10.11907/rjdk.171721
	You Xiaojian, Han Xuemei . Cognitive decision engine for cognitive radio based on the CSO algotithm[J]. Software Guide, 2017,16(7):128-130. doi: 10.11907/rjdk.171721
[6]	Martínez-Vargas A, Domínguez-Guerrero J, Andrade ángel G , et al. Application of NSGA-II algorithm to the spectrum assignment problem in spectrum sharing networks[J]. Applied Soft Computing, 2016,39(C):188-198. doi: 10.1016/j.asoc.2015.11.010
[7]	吴润泽, 高丽媛, 唐良瑞 , 等. 认知无线网Underlay模式下频谱资源优化分配[J]. 北京邮电大学学报, 2017,40(1):105-110. doi: 10.13190/j.jbupt.2017.01.019
	Wu Runze, Gao Liyuan, Tang Liangrui , et al. Optimal spectrum allocation of cognitive wireless network under Underlay mode[J]. Journal of Beijing University of Posts and Telecommunications, 2017,40(1):105-110. doi: 10.13190/j.jbupt.2017.01.019
[8]	董晓丹, 丁力, 郑雪芳 . 改进ABC算法求解认知无线网络频谱分配问题[J]. 机械设计与制造, 2017(1):231-234.
	Dong Xiaodan, Ding Li, Zheng Xuefang . Spectrum allocation mechanism based on improved ABC algorithm for cognitive wireless networks[J]. Machinery Design and Manufacture, 2017(1):231-234.
[9]	叶明杰 . 认知无线电网络中改进的拓扑控制算法[J]. 电子科技, 2015,28(5):13-16. doi: 10.16180/j.cnki.issn1007-7820.2015.05.004
	Ye Mingjie . An improved topology control algorithm in cognitive radio networks[J]. Electronic Science and Technology, 2015,28(5):13-16. doi: 10.16180/j.cnki.issn1007-7820.2015.05.004
[10]	Gong M G, Chen X W, Ma L J , et al. Identification of multi-resolution network structures with multi-objective immune algorithm[J]. Applied Soft Computing, 2013,13(4):1705-1717. doi: 10.1016/j.asoc.2013.01.018
[11]	Shang R H, Zhang W T, Wen A L, et al. On the use of immune clone optimization for unconstrained multi-objective resource allocation in the cognitive OFDMA networks [C].Vancouver:Evolutionary Computation, 2016.
[12]	Zhou Z R, Zhu J Y, Li J J, et al. Resource allocation based on immune algorithm in Multi-Cell cognitive radio networks with OFDMA [C].Shiyang:International Conference on Computational and Information Sciences, 2013.
[13]	Jiang H, Liu Y, Huang Y Q , et al. Optimization for cognitive radio waveform based on adaptive multi-objective immune genetic algorithm with vaccine injection[J]. Journal of the University of Electronic Science and Technology of China, 2015,44(2):183-189. doi: 10.3969/j.issn.1001-0548.2015.02.004
[14]	Deb K, Pratap A, Agarwal S , et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197. doi: 10.1109/4235.996017
[15]	Hwang J, Yoon H . A mixed spectrum management framework for the future wireless service based on techno-economic analysis: the Korean spectrum policy study[J]. Telecommunications Policy, 2009,33(8):407-421. doi: 10.1016/j.telpol.2009.04.005
[16]	Gong M G, Jiao L C, Du H F , et al. Multiobjective immune algorithm with nondominated neighbor-based selection[J]. Evolutionary Computation, 2008,16(2):225-255. doi: 10.1162/evco.2009.17.1.131 pmid: 19207091

参数	值
种群大小	100
迭代次数	100
主链路数量	4
次链路数量	20
主链路功率	10 mW
次链路功率	10 mW

认知无线电网络中基于免疫算法的多目标频谱分配

Spectrum Allocation with Multi-Objective Optimization based on Immune Algorithm in Cognitive Radio Networks

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