认知无线电分簇子网频谱决策  

Spectrum Decision Making in Clustering Cognitive Radio Subnet

作　　者：赵俊[1,2] 廖名学[1] 何晓新[1] 郑昌文[1] 

机构地区：[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 [2]中国科学院大学,北京100049

出　　处：《计算机科学》2014年第6期27-30,68,共5页Computer Science

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。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.

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 

分 类 号：TN92[电子电信—通信与信息系统]