CR网络中的主用户活动检测与跟踪算法研究

针对集中控制认知无线网络提出一种主用户活动估计与跟踪算法。首先,建立移动模型来预测可能的检测时间,然后认知基站选择预测检测时间最长的认知用户来检测主用户。其次,每个认知用户使用最大似然估计理论来估计主用户的ON/OFF平均周期。最后,认知基站使用均方误差来停止精细检测,并且利用均方误差的变化来重新启动精细检测以便跟踪主用户活动的变化。仿真结果表明,提出的方法能够快速有效地检测并跟踪主用户的活动。

Research on primary user activity detection and track algorithms in cognitive radio networks

A spectrum usage detection approach based on time prediction for centralized Cognitive radio （CR） networks is proposed.The proposed approach allows the CR users to learn about the activity of the PUs,and adapt to subsequent changes.CR base station selects CR user with the longest sensing time predicted by a mobile model.Each selected mobile CR user uses maximum likelihood estimator （MLE） on the observed ON/OFF period samples to estimate the average busy and idle periods.In addition,CR base station employs mean square error （MSE） to determine when the fine sensing should stop,and exploits the variation of MSE to restart the fine sensing.Simulation results reveal that our proposed method can efficiently and quickly track the dynamics of the PU spectrum usage.