面向认知无线电网络能量高效协作感知的在线节点选择算法

面向认知无线电网络中能量高效协作感知任务需求,提出了面向单次协作感知过程的能量最小化节点选择问题EMNS和面向在线协作感知的能量高效节点选择问题OENS。证明了两问题均为NP-hard难题。针对EMNS问题,提出采用分枝定界算法BAB求最优解和贪婪节点选择算法GS求近似解。针对OENS问题,提出为每个节点引入考虑能量消耗负载均衡的动态权重系数,基于BAB和GS算法设计了启发式的在线节点选择算法OBAB、OGS1。仿真实验结果表明,提出的算法可显著增加网络完成的协作感知过程次数,可有效延长网络"生存期"。

Online energy-efficient node selection algorithm for cooperative sensing in cognitive radio networks

To meet the requirement of the energy-efficient cooperative sensing applications in cognitive radio networks,the energy minimization node selection(EMNS) and the online energy-efficient node selection(OENS) problems were proposed,for one round and online cooperative sensing scenarios,respectively.Both problems were proved to be NP-hard.For EMNS,first a branch and bound algorithm BAB was proposed to find the optimal solution,then a greedy selection algorithm GS was designed to get a heuristic solution.For OENS,based on algorithms BAB and GS,two online algorithms OBAB and OGS1 were proposed,in which the weight coefficients were adopted that consider the load balancing of each node’s energy consumption.Extensive experiments by simulations demonstrate that the proposed algorithms can significantly increase the number of successful cooperative sensing processes,thereby increasing the network lifetime effectively.