一种基于物理-社交感知和支付激励的D2D多播内容共享策略

面向大规模用户的多媒体业务尤其是在线视频业务正呈现爆炸式发展的态势,D2D(Device-to-Device)多播内容共享技术被认为是一种可以有效应对大规模用户海量数据分发的关键技术。但目前关于D2D多播内容共享的研究多集中在如何提高系统的能量有效性上,对于系统数据速率和的研究不多,而系统数据速率和是反映系统能否高效分发内容的一个重要指标。为了建立一个贴近实际场景的用户模型并实现高效内容分发以减轻基站负担并提高资源(频谱和能量)利用效率,文中提出一种基于物理-社交感知和支付激励的D2D多播内容共享策略。首先,根据实际场景的限制对D2D多播通信进行建模,把模型的应用场景扩大到人流集中的高速内容共享的“热点”地区和不利于基站直接传输数据(如抗震救灾时)的大规模搜救行动的“盲点”地区。随后,以有效降低基站负载和应对海量数据分发为目标,提出以多约束条件下系统等效数据速率和为目标函数的优化问题,通过引入支付机制激励用户作为簇头为其他用户提供共享内容,通过引入基于兴趣相似度的社交关系来提高资源利用效率并降低用户支付代价。最后,提出簇头选择-簇形成算法来求解上述问题。在簇头选择算法中,在考虑用户数据速率阈值限制的同时,引入基于用户兴趣相似度的社交关系;在簇形成算法中,采用了一种增益定义与“联盟”内涵高度契合的集中控制式的联盟形成博弈模型。仿真结果表明,与相关同类策略相比,所提策略在等效数据速率和与实际数据速率和两项指标上的性能得到了显著提高,同时证明了该策略适合大规模用户的网络。

D2D Multicast Content Sharing Scheme Based on Physical-Social Awareness and Payment Incentive

Multimedia services,especially online video services,are explosively developing.D2D(Device-to-Device)multicast content sharing is considered as a key technology that can handle massive data delivery.However,most of the current researches on D2D multicast content sharing focus on how to improve the energy efficiency of the system,while there are few researches on the data rate sum of the system,which is an important index to reflect whether the system can efficiently distribute content.In order to establish a user model which is closer to the actual scene and implement efficient content delivery to alleviate the burden of Base Stations and improve the utilization efficiency of resources(spectrum and energy),this paper proposes a kind of D2D multicast content sharing scheme based on physical-social awareness and pay incentive.Firstly,D2D multicast communication is mode-led according to the limitations of the actual scene,and the application scene of the model is expanded to the“hot spot”area with content sharing at high data rate where people are concentrated and the“blind spot”area at which the data cannot be easily transmitted directly by Base Stations in earthquake relief operations.Then,in order to effectively reduce the load of Base Stations and to cope with huge amounts of data delivery,this paper puts forward the optimization problem that the system equivalent data rate sum is regarded as an objective function under multiple constraints.In the objective function,the payment mechanism is introduced to encourage users to provide shared content for other users as cluster heads,and social ties based on similarity of interest are introduced to reduce user payment cost and improve resource utilization efficiency.Finally,a cluster head selection-cluster formation algorithm is proposed to solve this problem.In the cluster head selection algorithm,social ties based on similarity of user interest is introduced while considering the limit of user data rate threshold.In the algorithm of cluster formation,a coalition formation game of centralized control is adopted,in which the gain definition is highly consistent with the connotation of“coalition”.The simulation results show that the performance of the proposed scheme on the equivalent data rate sum and actual data rate sum is significantly improved compared with the relevant similar scheme,and it is also proved that the proposed scheme is suita-ble for large-scale user networks.