An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
An Evolutionary Game Model of Resources-sharing Mechanism in P2P Networks
IITA '07 Proceedings of the Workshop on Intelligent Information Technology Application
Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks
IEEE Transactions on Wireless Communications
Spatiotemporal Sensing in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
Game theory for cognitive radio networks: An overview
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cooperative spectrum sensing in cognitive radio networks: A survey
Physical Communication
Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
A Survey of Cooperative Games for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
Optimal non-identical sensing setting for multi channels in cognitive radio networks
Computer Communications
Sequential multi-agent exploration for a common goal
Web Intelligence and Agent Systems
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Cooperative spectrum sensing has been shown to be able to greatly improve the sensing performance in cognitive radio networks. However, if cognitive users belong to different service providers, they tend to contribute less in sensing in order to increase their own throughput. In this paper, we propose an evolutionary game framework to answer the question of "how to collaborate" in multiuser de-centralized cooperative spectrum sensing, because evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions, adaptively learning during the strategic interactions, and approaching the best response strategy under changing conditions and environments using replicator dynamics. We derive the behavior dynamics and the evolutionarily stable strategy (ESS) of the secondary users. We then prove that the dynamics converge to the ESS, which renders the possibility of a de-centralized implementation of the proposed sensing game. According to the dynamics, we further develop a distributed learning algorithm so that the secondary users approach the ESS solely based on their own payoff observations. Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than the case where secondary users sense the primary user individually without cooperation. The proposed game is demonstrated to converge to the ESS, and achieve a higher system throughput than the fully cooperative scenario, where all users contribute to sensing in every time slot.