NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Dynamic Spectrum Access with QoS and Interference Temperature Constraints
IEEE Transactions on Mobile Computing
Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
Primary-prioritized Markov approach for dynamic spectrum allocation
IEEE Transactions on Wireless Communications
Steady-state Markov chain analysis for heterogeneous cognitive radio networks
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Cognitive radios for dynamic spectrum access: from concept to reality
IEEE Wireless Communications
A continuous-time Markov chain model and analysis for cognitive radio networks
International Journal of Communication Networks and Distributed Systems
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Sensing-Throughput Tradeoff for Cognitive Radio Networks
IEEE Transactions on Wireless Communications
Optimal spectrum sensing framework for cognitive radio networks
IEEE Transactions on Wireless Communications
Delay Performance Analysis for Supporting Real-Time Traffic in a Cognitive Radio Sensor Network
IEEE Transactions on Wireless Communications
Emerging cognitive radio applications: A survey
IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Existing spectrum sensing methods for cognitive radio do not consider the secondary network's characteristics to obtain the frequency of spectrum sensing, i.e., the sensing period would be identical for secondary networks that have different traffic characteristics. In this paper, a hybrid sensing algorithm is proposed that finds the optimal sensing period based on both primary and secondary networks' properties. A continuous-time Markov chain system is used to accurately model the spectrum occupancy, and a novel method is proposed that adaptively varies its parameters to avoid unnecessary sensing tasks, while guaranteeing the priority of the primary network. We conduct simulation work to evaluate the performance of the proposed method. It is shown that the proposed technique outperforms the non-hybrid approach with respect to sensing efficiency and energy consumption. A cognitive sensor network is also considered based on IEEE 802.15.4/ZigBee radios, and it is shown that significant energy savings can be achieved by the proposed method.