Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Tutorial on maximum likelihood estimation
Journal of Mathematical Psychology
Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
CRAHNs: Cognitive radio ad hoc networks
Ad Hoc Networks
A survey on MAC protocols for cognitive radio networks
Ad Hoc Networks
Search: A routing protocol for mobile cognitive radio ad-hoc networks
Computer Communications
OFDM-Based Common Control Channel Design for Cognitive Radio Ad Hoc Networks
IEEE Transactions on Mobile Computing
Cooperative spectrum sensing in cognitive radio networks with weighted decision fusion schemes
IEEE Transactions on Wireless Communications
Temporal spectrum sharing based on primary user activity prediction
IEEE Transactions on Wireless Communications
IEEE/ACM Transactions on Networking (TON)
Optimal spectrum sensing framework for cognitive radio networks
IEEE Transactions on Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
Low-Complexity Adaptive Transmission for Cognitive Radios in Dynamic Spectrum Access Networks
IEEE Journal on Selected Areas in Communications
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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.