An indirect reciprocity based incentive framework for cooperative spectrum sensing

To overcome the hidden terminal problem a secondary user (SU) may encounter, cooperative spectrum sensing (CSS) is proposed and gained much attention in the last decades. However, due to the selfish nature, SUs may not cooperate unconditionally as most previous works have assumed. Therefore, how to stimulate SUs to play cooperatively is an important issue. In this paper, we propose a reputation-based CSS incentive framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed game, SUs choose how to report their sensing results to the fusion center (FC) and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future. For the proposed game, we derive theoretically the optimal action rule, according to which the SU will truthfully report its result when the estimated average energy is equal to or higher than the given threshold and vice versa. The decision accuracy of the FC thereby can be greatly improved. Moreover, we derive the condition under which the optimal action rule is evolutionarily stable. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.