Liu Xin
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2015 Oct 30;15(11):27760-82. doi: 10.3390/s151127760.
In a cognitive sensor network (CSN), the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs) becomes very large. In this paper, a novel wireless power transfer (WPT)-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF) energy of the primary node (PN) to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.
在认知传感器网络（CSN）中，当协作认知节点（CN）的数量变得非常大时，传感时间和能量的浪费对协作频谱感知构成了挑战。本文提出了一种基于新型无线功率传输（WPT）的加权聚类协作频谱感知模型，该模型将所有CN划分为几个簇，然后选择最有利的CN作为簇头，并允许普通CN将接收到的主节点（PN）的射频（RF）能量传输给簇头，以便提供传感和协作所需的电能。通过联合分配传感时间和聚类数量，制定了联合资源优化方案，以最大化CSN的频谱接入概率。根据资源优化结果，提出了一种聚类算法。仿真结果表明，与传统模型相比，所提模型的簇头能够获得更多的传输功率，并且存在最优的传感时间和聚类数量以最大化频谱接入概率。
