ABSTRACT
Ning Zhang, Haibo Zhou, Shaohua Wu, Ying Wang, Jon W. Mark, and Xuemin (Sherman) Shen
CONTENTS 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 8.2 Cooperation-Based Dynamic Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
8.2.1 Cooperative Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 8.2.2 Cooperative cognitive radio networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
8.3 Cooperative Spectrum Sensing in Multi Channel Environments . . . . . . . . . . . . . . . . . . . . 194 8.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 8.3.2 Spectrum Sensing in Multi-channel CRNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
8.3.2.1 Cooperative Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 8.3.2.2 Sensing Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 8.3.2.3 Cross-Entropy-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
8.3.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 8.4 Cooperative Cognitive Radio Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
8.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 8.4.2 Partner Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 8.4.3 Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
8.4.3.1 Utility Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 8.4.3.2 Noncooperative Power Selection Game . . . . . . . . . . . . . . . . . . . . . . 202 8.4.3.3 Source Node Utility Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . 203
8.4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Cognitive radio network is a promising solution to solve the spectrum scarcity problem by allowing dynamic spectrum sharing between unlicensed users and licensed users. In order to avoid interfering
with licensed users, unlicensed users need to perform spectrum sensing. However, spectrum sensing might be inaccurate due to multipath fading and shadowing. Therefore, cooperation based dynamic spectrum sharing are introduced. Specifically, when unlicensed users want to transmit, they can be coordinated to cooperatively sense the spectrum bands to maximize the total expected available time. The coordination problem is formulated as a nonlinear integer programming problem, which is proved to be NP-complete. Then, the problem is first transformed to an associated stochastic optimization problem, which is solved by cross-entropy (CE) method of stochastic optimization. When unlicensed users are idle, they can earn credits by acting relays for licensed users to improve the latter’s performance such as the secrecy. The earned credits can be utilized for spectrum trading in the future when they have traffic. The procedure of payment negotiation and transmission power allocation is modeled by Stackelberg game. By analyzing the game, the unlicensed users can determine the transmission powers for cooperation, while the licensed user can select the best payment. Finally, simulation results are provided to demonstrate the performance of the cooperation based proposed schemes.
