基于量子布谷鸟搜索的认知无线网络频谱分配

doi:10.11959/j.issn.1000-0801.2016125

Abstract

Abstract:

There are discrete optimization problems for spectrum allocation in cognitive wireless network.A novel combinatorial optimization algorithm called quantum cuckoo search algorithm （QCSA）was proposed,which was based on quantum computing and cuckoo search algorithm.The quantum nest was used to represent multiple dimensionality solution for the optimization problem,and the global optimal position was found according to Lévy flights and quantum mutation strategy.In additional,some classical benchmark functions were employed to prove the effectiveness of QCSA,and a spectrum allocation method based on QCSA was proposed for cognitive network.Compared with classical spectrum allocation methods by using different network utility functions,the global optimal solution can be searched so fast.Simulation results show that the proposed spectrum allocation method based on QCSA is better than other traditional methods under different network utility functions.

Key words: cognitive wireless network, spectrum allocation, discrete optimization problem, quantum computing, cuckoo search algorithm

Xianping WANG,Hui CAO. Spectrum allocation based on quantum cuckoo search algorithm in cognitive radio network[J]. Telecommunications Science, 2016, 32(5): 62-68.

Figures/Tables 8

References 16

[1]	AKYILD Z , LI W . Next generation/dynamic spectrum access/cognitive radio wireless networks:a survey. Journal of Computer Networks, 2013,9(2): 2127-2159.
[2]	JOSEPH M . Cognitive radio for flexible mobile multimedia communication[C]// Sixth International Workshop on Mobile Multimedia Communications， Nov 15-17, 1999, SanDiego,CA,USA. New Jersey: IEEE Press, 1999: 3-10.
[3]	廖楚林，陈吉，唐友喜等. 认知无线电中的并行频谱分配算法[J]. 电子与信息学报 2007,29(7):1608-1611. LIAO C L ， CHEN J ， TANG Y X et al. Parallel algorithm of spectrum allocation in cognitive radio networks[J]. Journal of Electronics & Information Technology, 2007,29(7):1608-1611.
[4]	WANG B B , WU Y L , LIU K J R . Game theory for cognitive radio networks:an overview[J]. Computer Networks, 2010,54(14): 2537-2561.
[5]	KIM S . Trust-based bargaining game model for cognitive radio spectrum sharing scheme[J]. IEICE Transactions on Communications, 2012,E95-B(12): 3925-3928.
[6]	PENG C , ZHENG H , ZHAO B Y . Utilization and fairness in spectrum assignment for opportunistic spectrum access[J]. ACM Mobile Network and Applications （MONET）, 2006,11(4): 555-576.
[7]	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 Y ， ZHENG S L et al. Cognitive radio spectrum assignment based on quantum genetic algorithm[J]. Acta Physica Sinica, 2009,58(2):1358-1363.
[8]	GAO H Y , CAO J L . Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation. Journal of Central South University, 2013,20(7): 1878-1888.
[9]	高洪元，李晨琬 . 膜量子蜂群优化的多目标频谱分配[J]. 物理学报 2014,63(12):128801-128812. GAO H Y ， LI C W . Membrane-inspired quantum bee colony algorithm for multi objective spectrum allocation[J]. Acta Physica Sinica, 2014,63(12):128801-128812.
[10]	YANG X S , DEB S . Cuckoo search via Lévy flights[C]// World Congress on Nature&Biologically Inspired Computing， December 9-11, 2009, Coimbatore,India.[S.l.:s.n.], 2009: 210-214.
[11]	YANG X S , DEB S . Engineering optimization by Cuckoo search[J]. International Journal of Mathematical Modeling and Numerical Optimization, 2010,1(4): 330-343.
[12]	CIVICIOGLU P , BESDOK E . A conceptual comparison of the cuckoo search,particle swarm optimization,differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 2013,39(4): 315-346.
[13]	宋玉坚，叶春明，黄佐研 . 基于克隆布谷鸟算法的资源均衡优化[J]. 计算机应用研究 2014,31(5):1324-1327. SONG Y J ， YE C M ， HUANG Z Y . Resource leveling optimization based on clonal cuckoo search algorithm[J]. Application Research of Computers, 2014,31(5):1324-1327.
[14]	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 network based on immune clone selection optimization[J]. Journal on Communication, 2010 31(11):92-100.
[15]	彭振，赵知劲，郑仕链 . 基于混合蛙跳算法的认知无线电频谱分配[J]. 计算机工程 2010,36(6):210-213. PENG Z ， ZHAO Z ， ZHENG S L . Cognitive radio spectrum assignment based on shuffled frog leaping algorithm[J]. Computer Engineering, 2010 36(6):210-213.
[16]	张勇，夏树发，唐冬生 . 果蝇优化算法对多峰函数求解性能的仿真研究[J]. 暨南大学学报（自然科学与医学版）, 2014,35(1):82-87. ZHANG Y ， XIA S F ， TANG D S . Simulation of multi-peak function based on the fly optimization algorithm[J]. Journal of Jinan University（Natural Science & Medicine Edition）, 2014 35(1):82-87.

Metrics

Recommended 0

No Suggested Reading articles found!

算法	频段数目
算法	10	12	14	16	18
CSGC	26.012	30.238	32.463	42.879	48.254
QGA	28.176	35.213	39.218	45.322	57.298
QABC	28.587	35.490	39.456	45.415	57.630
QPSO	28.742	35.625	39.712	45.729	57.831
QCSA	29.149	35.918	40.581	45.931	59.210

算法	次级用户数目
算法	10	12	14	16	18
CSGC	26.012	21.432	19.298	16.276	15.132
QGA	28.176	21.576	19.415	16.746	15.471
QABC	28.587	21.681	19.732	16.912	15.634
QPSO	28.742	22.023	19.980	17.026	16.450
QCSA	29.149	23.538	21.320	18.540	17.203

算法	频段数目
算法	10	12	14	16	18
CSGC	15.014	22.436	28.798	32.105	35.329
QGA	20.342	26.198	30.989	40.142	44.591
QABC	20.465	26.321	31.210	40.327	44.750
QPSO	20.762	26.957	31.476	40.648	44.954
QCSA	21.320	27.048	32.450	42.620	47.219

算法	次级用户数目
算法	10	12	14	16	18
CSGC	15.014	8.673	5.209	2.409	1.098
QGA	20.342	16.425	13.183	10.024	8.104
QABC	20.465	16.689	13.539	10.316	8.461
QPSO	20.762	16.702	13.742	10.753	8.632
QCSA	21.320	18.280	15.218	12.014	10.983

Spectrum allocation based on quantum cuckoo search algorithm in cognitive radio network

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 16

Related Articles 10

Metrics

Recommended 0

[1]	Wenjuan XING, Bo LEI, Qianying ZHAO. Development status and trend prospect of computing power infrastructure [J]. Telecommunications Science, 2022, 38(6): 51-61.
[2]	·iping ZUO,Shi JIN,Shengli ZHANG. Research status and development trend of blockchain in cellular mobile communication system [J]. Telecommunications Science, 2019, 35(9): 114-123.
[3]	Yawen ZHANG,Miao WANG,Linghao WANG,Hanwen ZHANG,Yujun ZHANG. Fixed features of future networks based on future applications [J]. Telecommunications Science, 2019, 35(8): 65-73.
[4]	Biyou ZHAO,Shanhui ZHANG,Jinshuai WANG. Spectrum allocation algorithm of elastic optical networks in power distribution system [J]. Telecommunications Science, 2019, 35(2): 43-50.
[5]	Chang CAI,Yafang WANG,Bingmei MIAO,Hui JIANG. Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm [J]. Telecommunications Science, 2017, 33(8): 85-93.
[6]	Ling LIU,Yongju XIAN,Jia ZHU. Multi-type spectrum resources joint distribution algorithm based on guaranteed QoS [J]. Telecommunications Science, 2016, 32(1): 46-53.
[7]	Wei Chen. When multiple cooperative communication: virtual real-time service theory and application [J]. Telecommunications Science, 2014, 30(7): 43-52.
[8]	Jiongtao Wang,Ming Jin,Youming Li,Jingjing Zheng. A Double-Threshold Based Spectrum Allocation Algorithm in Cognitive Radio System [J]. Telecommunications Science, 2013, 29(8): 127-131.
[9]	Huajing Zhang,Shaoyi Xu,Xiaoyu Qiao. Dynamic Spectrum Allocation Algorithm Based on User Demands and Spectrum Aggregation in Cognitive Radio Networks [J]. Telecommunications Science, 2010, 26(12): 63-67.
[10]	Dexiang Meng,Xiaochao Xu. Researches On Spectrum ResOurce fOr TD-SCDMA EvOlutiOn [J]. Telecommunications Science, 2009, 25(1): 32-37.