计算机科学 ›› 2014, Vol. 41 ›› Issue (6): 27-30.doi: 10.11896/j.issn.1002-137X.2014.06.006

• 网络与通信 • 上一篇    下一篇

认知无线电分簇子网频谱决策

赵俊,廖名学,何晓新,郑昌文   

  1. 中国科学院软件研究所天基综合信息系统重点实验室 北京100190;中国科学院大学 北京100049;中国科学院软件研究所天基综合信息系统重点实验室 北京100190;中国科学院软件研究所天基综合信息系统重点实验室 北京100190;中国科学院软件研究所天基综合信息系统重点实验室 北京100190
  • 出版日期:2018-11-14 发布日期:2018-11-14

Spectrum Decision Making in Clustering Cognitive Radio Subnet

ZHAO Jun,LIAO Ming-xue,HE Xiao-xin and ZHENG Chang-wen   

  • Online:2018-11-14 Published:2018-11-14

摘要: 树形认知无线电分簇子网采用多簇并行工作模式,其频谱决策涉及子网容量、吞吐量与子网稳定性3方面因素,计算复杂度高。针对多簇子网的频谱决策问题,建立了三层优先级决策模型,并提出一种启发式决策算法。该算法基于簇结构和簇生长度构造无重复的搜索空间,并以当前最优解更新的搜索步长为启发式条件,贪心搜索增长率更高的子网结构,引入子网容量下限、可用频谱及子网速率双门限,对解空间进行严格剪枝。仿真结果表明,在相应频谱空间和子网规模等约束条件下,该算法能够获得最优解且满足实时性需求。

关键词: 认知无线电,频谱决策,树形网络,多目标优化,回溯算法 中图法分类号TN92文献标识码A

Abstract: Tree cognitive radio subnets employ multi-cluster parallel operation mode.Its spectrum decision involves subnet capacity,subnet throughput and subnet stability,leading to high compute complexity.To solve the spectrum decision making problem,a triple-layer priority decision model was established and a corresponding heuristic decision algorithm was proposed.The proposed algorithm creates a search space without generating duplicated nodes via cluster structure and cluster growth degree,greedily searching for subnet structure with higher growth rate on the heuristic condition that is the search steps of updating the optimal solution.The solution space is rigorously pruned at double thresholds,i.e.,subnet capacity limit and the available spectrum and subnet transmission rate limit.Simulation results show that the proposed algorithm can obtain the optimal solution and meet real-time requirement under a specified subnet scale and spectrum space constraints.

Key words: Cognitive radio,Spectrum decision,Tree-based network,Multi-objective optimization,Backtracking algorithm

[1] European Radio communications Committee (ERC).Europeantable of frequency allocations and utilizations frequency range 9kHz to 275GHz[R].ERC Report 25,January 2002
[2] Natasha D,Mai Vu,Vahid T.Cognitive Radio Networks [J].IEEE Signal Processing Magazine,2008,25(6):12-23
[3] Akyildiz I F,Lee W Y,Vuran M C,et al.A survey on spectrum management in cognitive radio networks[J].Communications Magazine,IEEE,2008,46(4):40-48
[4] Talat S T,Wang L C.Load-Balancing Spectrum Decision forCognitive Radio Networks with Unequal-Width Channels[C]//Vehicular Technology Conference (VTC Fall),2012IEEE.IEEE,2012:1-5
[5] Liao M X,He X X,Jiang X H.Optimal Algorithm for Cognitive Spectrum Decision Making[C]//COCORA 2012,The Second International Conference on Advances in Cognitive Radio.2012:50-56
[6] 杨云,章国安,邱恭安.认知无线Mesh网络中基于概率的贪婪频谱决策技术研究[J].计算机科学,2012,39(B06):163-165
[7] Tsagkaris K,Katidiotis A,Demestichas P.Neural network-based learning schemes for cognitive radio systems[J].Computer Communications,2008,31(14):3394-3404
[8] 张北伟,胡琨元,朱云龙.基于博弈论的认知无线电频谱分配[J].计算机应用,2012,32(9):2408-2411
[9] Chung S T,Kim S J,Lee J,et al.A game-theoretic approach to power allocation in frequency-selective Gaussian interference channels[C]∥Proc.IEEE International Symposium on Inform.Theory.Pacifico,2003
[10] Zhang W.Handover decision using fuzzy MADM in heteroge-neous networks[C]∥Wireless Communications and Networking Conference.WCNC 2004IEEE.IEEE,2004,2:653-658
[11] 瞿越,鲜永菊,徐昌彪.基于用户需求的图着色论频谱分配算法[J].计算机应用,2011,31(3):602-605
[12] 吴非,陈劼,廖楚林,等.认知无线电网络中基于需求的多小区频谱分配算法[J].计算机应用,2008,28(1):14-16
[13] 张北伟,朱云龙,胡琨元.基于粒子群算法的认知无线电频谱分配算法[J].计算机应用,2011,31(12):3184-3186
[14] Fan Zhong-ji,Liao Ming-xue,He Xiao-xin,et al.Efficient Algorithm for Extreme Maximal Biclique Mining in Cognitive Frequency Decision Making[C]∥2011IEEE 3rd International Conference on Communication Software and Networks (ICCSN).IEEE,2011:25-30
[15] Ji Pan-pan,Liao Ming-xue,He Xiao-xin,et al.Extreme Maximal Weighted Frequent Itemset Mining for Cognitive Frequency Decision Making[C]∥2011International Conference on Computer Science and Network Technology (ICCSNT).IEEE,2011,1:267-271

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