ID 原文 译文
3403 为了解决城市环境下的 C-V2X 车辆拓扑高度动态化且车辆节点负载能力有限的问题，提高车辆缓存的利用率，减轻基站负荷，提出了负载约束下的车辆缓存节点选择算法。 In order to solve the problem that the C-V2X vehicle topology in urban environment was highly dynamic andthe load capacity of vehicle nodes was limited, and improve the utilization of vehicular cache resources and reduce theload of base station, a vehicle cache nodes selection algorithm under load constraints was proposed.
3404 首先，通过定义链路稳定性度量，构建预测权重邻接矩阵，微观地描述车辆拓扑关系； Firstly, by definingthe link stability metric, the predicted weight adjacency matrix was constructed to describe the vehicular micro-topologyin essence.
3405 其次，在负载约束和无重叠覆盖约束下构建目标函数，以最少的缓存节点实现全覆盖且最大化簇平均链路权重； Next, the objective function was further constructed under the load constraints and non-overlapping coverage constraint, which maximized the average link weight of the clusters by using the least cache nodes.
3406 最后，引入贪婪思想并合理定义节点状态，求解负载约束下车辆拓扑的最小支配集，并择优选择服务邻居节点。 Finally, the greedyconcept was then introduced and the node states were reasonably defined. As a result, the minimum dominating set of thevehicle topology was figured out under the load constraints. Besides, the serviced neighbor nodes were then determined preferentially.
3407 仿真结果表明，所提算法在缓存节点个数和簇平均链路权重均值方面接近全局最优，其重复应答率恒为零，请求应答率可达理论上界并可有效提高缓存源应答次数。 The simulation results show that the proposed algorithm is close to the global optimal results in terms of the number of cache nodes and the average weight of cluster links. Moreover, the repeated response ratio of the proposedalgorithm is always zero while the request response ratio can achieve the theoretical upper bound. Furthermore, the re-sponse times of cache resources can be also effectively improved.
3408 为了满足 5G 系统低时延高可靠的需求，针对单缓存终端直传（D2D）协作边缘缓存系统，提出了一种基于传输时延的缓存策略。 To meet the requirements of 5G system with low delay and high reliability, for a single-caching D2D coopera-tive edge caching system, an optimization of the caching strategy based on transmission delay was proposed.
3409 运用随机几何理论，将请求用户和空闲用户的动态分布建模为相互独立的齐次泊松点过程， The dy-namic distributions of requesting users and idle users were modeled as the independent homogeneous poisson pointprocess (HPPP) utilizing the stochastic geometry theory.
3410 综合考虑内容流行度、用户位置信息、设备传输功率以及干扰，推导出用户的平均传输时延与缓存概率分布的关系式。 Comprehensively considering the content popularity, user loca-tion information, device transmission power and interference, the relationship between the average transmission delay ofuser and the cache probability distribution was derived.
3411 以平均传输时延为目标函数建立优化问题，提出了一个低复杂度的迭代算法，得到平均传输时延次优的缓存策略。 Taking the average transmission delay as the objective function,the optimization problem was established, and a low complexity iterative algorithm was proposed to obtain the cachestrategy with sub-optimal average transmission delay.
3412 仿真结果表明，该缓存策略在传输时延方面优于常见的几种缓存策略。 Simulation results demonstrate that the proposed optimizationcache strategy is superior to several common cache strategies in terms of transmission delay.