基于社交意识和支付激励的D2D协作传输策略

D2D cooperative transmission strategy based on social awareness and payment incentive

在D2D协作传输场景下,传统的协作方案忽略了节点的自私性及在每个时间点个性化需求的差异性,导致D2D用户配对成功率较低。为了解决上述问题,提出一种基于社交意识和支付激励的D2D协作传输策略。具体地,首先利用物理层终端用户间相遇的历史信息,计算不同时刻用户间的社会关系强度;接着通过节点属性、节点对内容兴趣度计算出内容对节点的重要程度;其次该方案采用熵定义用户服务属性等级,根据节点对内容的不同服务属性等级对不同服务主体进行区别定价,以激励自私节点参与协作;然后用以上因素来构造效用函数,使系统总效用最大化;最后采用模拟退火算法求解从而得到一个稳定的协作传输策略。仿真结果表明,所提策略可满足不同时间段终端个性化需求,有效提高用户满意度和吞吐量。

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 attri-butes 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.