协作频谱感知中快速自适应门限策略

频谱感知是认知无线电的一个重要组成部分。在异构网络中，认知节点的移动会导致接收信号强度和噪声功率发生变化，这使得采用固定门限参数的频谱感知策略无法保证在任何时候均工作于最优感知状态。为了解决这一问题，该文提出一种自适应门限参数的协作频谱感知策略。该策略无需主用户信号、信道以及环境噪声的任何先验信息，参与协作的所有认知节点采用最陡下降法自适应调节门限参数，控制中心采用最优数据融合算法获得最小检测代价。仿真结果显示，当认知节点参数发生变化时，协作节点的门限参数快速收敛于最优值，使系统贝叶斯风险最小。

Fast Adaptive Threshold for Cooperative Spectrum Sensing

Spectrum sensing is a key functional part for cognitive radio networks. In heterogeneous networks, the mobility of cognitive nodes will lead to changes in the received signal strength and noise power, which make it difficult for cognitive users to achieve optimal sensing performance at all times using traditional spectrum sensing methods with fixed thresholds. To solve this problem, an adaptive threshold scheme is proposed in this paper. The Steepest Descent Algorithm (SDA) is used to adjust thresholds of all cooperative nodes and the optimal data fusion rule is adopted in the control center to decrease the average Bayesian risk. No prior information of primary signals, channel fading and noise power is needed and the optimal sensing performance is achieved by applying the proposed scheme. Simulation results confirm that the proposed method can quickly converge to optimal sensing parameters in spatial temporal varying environment.