Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
