Licensed user activity estimation and track in mobile cognitive radio ad hoc networks

In cognitive radio (CR) networks, a static activity model fails to capture the dynamic and time-varying behavior of the licensed or primary users (PUs). In this paper, a distributed scheme is proposed that allows mobile CR users to learn about the activity of the PUs, and disseminate this information to the neighboring nodes that also function as information repositories. In order to guarantee sensing precision and transmission efficiency, the proposed method switches between time-intensive “fine sensing” and quick “normal sensing”. Our approach uses the maximum likelihood estimator to learn average busy and idle periods in the fine sensing stage. These identified activity patterns are then used during normal sensing, where the mean square error (MSE) value of PU on–off times is continuously monitored to ensure that the estimation is sufficiently accurate. When PU activity changes significantly, the MSE is considered as the indicator to re-start the fine sensing. Simulation results reveal that our proposed method can efficiently track the dynamics of the PU activity.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► We propose a mobile sensing model to estimate and track the licensed user activity. ► Weighted average approach can increase the estimation precision of ON and OFF average periods. ► Propose a fine sensing restart mechanism that can quickly track the change of licensed user activity. ► We investigate the influence of the numbers of period samples and sensing secondary users on the detection.