In the D2D cooperative transmission scenario, the traditional cooperation scheme ignores the selfishness of nodes and the difference of personalized requirements at each time point, resulting in a low success rate of D2D user pairing. To solve the above problems, this paper proposed a D2D collaborative transmission strategy based on social awareness and payment incentives. Specifically, it used the historical information of physical end users encounter to calculate the social relationship strength between users at different times. Then it calculated the importance of the content to the node through the node attributes and the node's interest in the content. Secondly, this scheme used entropy to define user service attribute levels, and differentiated pricing for different service subjects according to different service attribute levels of nodes to content, so as to encourage selfish nodes to participate in collaboration. Then it used the above factors to construct the utility function to maximize the total utility of the system. Finally it used the simulated annealing algorithm to solve it to obtain a stable cooperative transmission strategy. The simulation results show that the proposed strategy can meet the personalized needs of terminals in different time periods, and effectively improve user satisfaction and throughput.

[2023-01-05] Accepted Paper /doc/2022.10.0520/v1

[2023-06-05] Printed Article /doc/2022.10.0520/v2