无线认知传感器网络中基于时空相关性的协作频谱感知算法

Cooperative spectrum sensing algorithm based on temporal-spatial correlation in wireless cognitive sensor networks

无线应用的快速扩张使得未授权的工业、科学和医疗(ISM)频段十分拥挤,频谱资源稀缺已成为无线传感器网络发展的瓶颈。无线认知传感器网络允许传感器节点感知主用户的频谱空洞,并利用空闲频段进行通信而改善网络的通信能力。提出了一种基于时空相关性的协作频谱感知算法,该算法建立基于自回归模型的超采样序贯能量检测器以快速获得单节点的局部判决结果,然后为各节点分配时空相关的权重因子以进行加权序贯协作频谱感知,并给出精确的全局判决结果。仿真结果说明,相比于传统的序贯协作频谱感知算法,该算法可使用更少的采样数据量获得更优的频谱检测性能,从而以能量有效的方式提高网络的频谱利用率和数据传输效率。

Wireless sensor networks( WSNs) currently operate in the unlicensed industrial,scientific and medical( ISM) spectrum band. The rapid proliferation of wireless applications,however,has made this band overcrowded. The spectrum scarcity has been the bottleneck for the popularization of WSNs. Cognitive radio is a new paradigm that allows sensor nodes to sense spectrum hole,and then utilizes the vacant frequencies to improve the spectrum utilization rate and communication validity. In this paper,a new cooperative spectrum sensing algorithm is proposed based on temporal-spatial correlation in wireless cognitive sensor networks( WCSNs). A supersample sequential energy detector operating on the auto-regressive model is designed to quickly obtain the local detection result of an individual node. Furthermore,a weighted sequential cooperative spectrum sensing method is investigated to make the precisely global decision,which allocates a weighted factor for each node based on temporal-spatial correlation. Comparing with the traditional sequential cooperative spectrum sensing,simulation results show that the proposed algorithm can achieve higher detection accuracy with the reduced number of samples,which improves spectrum utilization rate and data transmission efficiency by energy-efficient manner.