重庆大学通信工程学院,重庆,400044
网络首发:2016-02-10,
纸质出版:2016
移动端阅览
朱冰莲, 朱方方, 段青言, 等. 采用多策略离散人工蜂群的改进频谱分配算法[J]. 西安交通大学学报, 2016,50(2):20-25+84.
An Improved Spectrum Allocation Algorithm Using Multi-Strategy Discrete Artificial Bee Colony Technology[J]. 2016, 50(2): 20-25+84.
朱冰莲, 朱方方, 段青言, 等. 采用多策略离散人工蜂群的改进频谱分配算法[J]. 西安交通大学学报, 2016,50(2):20-25+84. DOI: 10.7652/xjtuxb201602004.
An Improved Spectrum Allocation Algorithm Using Multi-Strategy Discrete Artificial Bee Colony Technology[J]. 2016, 50(2): 20-25+84. DOI: 10.7652/xjtuxb201602004.
针对图论频谱分配模型下最优频谱分配策略搜索解困难、耗时长的问题
提出一种采用多策略离散人工蜂群的频谱分配算法。首先
根据感知技术得到的通信环境状况
建立频谱分配的图论模型; 然后
引入多策略离散人工蜂群算法进行最优频谱分配策略的搜索
在搜索初期
引入全局探索能力强的粗搜索策略
以快速优化初始种群
后期以高精度的单维更新进行精细搜索; 考虑到仅当解参数值取1才能带来网络收益的增加
提出仅对取值为零的维度进行更新的策略
增强了搜索的有向性与有效性。仿真实验表明:该算法与当前基于离散人工蜂群和二进制粒子算法的频谱分配算法相比
无论是收敛速度还是网络收益都得到提高; 当可用频谱数在5~20之间、次用户数量在5~22之间变化时
获得相同最大收益的耗时仅为对比算法的47.75%~36.18%
且随着问题规模增加耗时呈下降趋势。
An improved spectrum allocation(MDABC-SA)algorithm using the multi-strategy discrete artificial bee colony technology is proposed to reduce computational time of spectrum allocation based on graph model. First
a spectrum allocation model is established based on parameters obtained by sensing technology. Then
the multi-strategy discrete artificial bee colony technology is employed to find the optimal spectrum allocation scheme
and a global searching operator is used in initial searches to rapidly find a better initial population
An one-dimensional search is then used in later searches to perform fine line search. The strategy to update only the elements with value of 0 is proposed to inhance the direction and effectiveness of searches by considering the fact that the more ‘1' have in the solution
the higher network utilization can be achieved. Simulation results and comparisons with the spectrum allocation algorithms using DABC and BPSO algorithms show that the proposed algorithm obviously improves both the convergence speed and network utilization. The algorithm achieves the same maximum benefit with only 47.75%-36.18% of consumed time of the former two algorithms when the number of available spectrum is between 5 and 20 and the number of secondary users varies from 5 to 22 and a downward trend in consumed time is observed when the problem scale increases.
CHIANG R I, ROWE G B, SOWERBY K W. A quantitative analysis of spectral occupancy measurements for cognitive radio [C]∥ Proceedings of the IEEE 65th Vehicular Technology Conference. Piscataway, NJ, USA: IEEE, 2007: 3016-3020.
陈鹏, 邱乐德, 王宇. 卫星认知无线电检测门限与功率分配联合优化算法 [J]. 西安交通大学学报, 2013, 47(6): 31-36, 43.
CHEN Peng, QIU Lede, WANG Yu. Joint optimization algorithm of detection threshold and power allocation for satellite underlay cognitive radio [J]. Journal of Xi'an Jiaotong University, 2013, 47(6): 31-36, 43.
李晓艳, 张海林, 郭超平, 等. 一种异步的认知无线电网络跳频算法 [J]. 西安交通大学学报, 2012, 46(12): 30-35.
LI Xiaoyan, ZHANG Hailin, GUO Chaoping, et al. Asynchronous channel hopping algorithm for cognitive radio networks [J]. Journal of Xi'an Jiaotong University, 2012, 46(12): 30-35.
王兵, 白智全, 董培浩, 等. 采用空时分组编码的动态分组加权合作频谱感知方案 [J]. 西安交通大学学报, 2014, 48(8): 23-28.
WANG Bing, BAI Zhiquan, DONG Peihao, et al. A spectrum sensing scheme with weighted collaboration of dynamical clustering using space-time block code [J]. Journal of Xi'an Jiaotong University, 2014, 48(8): 23-28.
TRAGOS E Z, ZEADALLY S, FRAGKIADAKIS A G, et al. Spectrum assignment in cognitive radio networks: a comprehensive survey [J]. IEEE Communications Surveys Tutorials, 2013, 15(3): 1108-1135.
ZHAO Z, PENG Z, ZHENG S, et al. Cognitive radio spectrum allocation using evolutionary algorithms [J]. IEEE Transactions on Wireless Communications, 2009, 8(9): 4421-4425.
PENG C, ZHENG H, ZHAO B Y. Utilization and fairness in spectrum assignment for opportunistic spectrum access [J]. Mobile Networks and Applications, 2006, 11(4): 555-576.
FRAGKIADAKIS A G, TRAGOS E Z, ASKOXYLAKIS I G. A survey on security threats and detection techniques in cognitive radio networks [J]. IEEE Communications Surveys Tutorials, 2013, 15(3): 428-445.
GHASEMI A, MASNADI-SHIRAZI M A, BIGUESH M, et al. Spectrum allocation based on artificial bee colony in cognitive radio networks [C]∥ Proceedings of 2012 Sixth International Symposium on Telecommunications. Piscataway, NJ, USA: IEEE, 2012: 182-187.
李鑫滨, 刘磊, 马锴. 基于离散人工蜂群算法的认知无线电频谱分配 [J]. 系统工程与电子技术, 2012, 34(10): 2136-2141.
LI Xinbin, LIU Lei, MA Kai. Cognitive radio spectrum allocation based on discrete artificial bee colony algorithm [J]. Systems Engineering and Electronics, 2012, 34(10): 2136-2141.
NAEEM M, ANPALAGAN A, JASEEEMUDDIN M, et al. Resource allocation techniques in cooperative cognitive radio networks [J]. IEEE Communications Surveys Tutorials, 2014, 16(3): 729-744.
KARABOGA D, BASTURK B. On the performance of artificial bee colony(ABC)algorithm [J]. Applied Soft Computing, 2008, 8(1): 687-697.
MARINAKIS Y, MARINAKI M, MATSATSINIS N. A hybrid discrete artificial bee colony-GRASP algorithm for clustering [C]∥ Proceedings of International Conference on Computers Industrial Engineering. Piscataway, NJ, USA: IEEE, 2009: 548-553.
GAO W, LIU S, HUANG L. A global best artificial bee colony algorithm for global optimization [J]. Journal of Computational and Applied Mathematics, 2012, 236(11): 2741-2753.
CREPINSEK M, LIU S, MERNIK M. Exploration and exploitation in evolutionary algorithms: a survey [J]. ACM Computing Surveys, 2013, 45(3): 35-38.
宁爱平, 张雪英. 人工蜂群算法的收敛性分析 [J]. 控制与决策, 2013(10): 1554-1558.
NING Aiping, ZHANG Xueying. Convergence analysis of artificial bee colony algorithm [J]. Control and Decision, 2013(10): 1554-1558.
0
浏览量
4
下载量
7
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621