| ID | 原文 | 译文 |
| 5144 | 在车联网中，链路频繁切换和信道干扰会导致传输时延增加及网络吞吐量下降。 | In vehicular Ad Hoc network (VANET), frequent link handovers and channel interference can lead to increasedtransmission delay and decreased network throughput. |
| 5145 | 引入无人机与地面车辆进行协同通信，构建无人机辅助的空地一体化车联网，采用发送端自主决策的分布式策略，提出了一种干扰感知的节点接入机制。 | To address the issues, unmanned aerial vehicle (UAV) were intro-duced to cooperate with vehicles and construct UAV-assisted air-ground integrated VANET. An interference-aware nodeaccess scheme was proposed. |
| 5146 | 将节点接入问题形式化为一个与链路传输速率、链路切换次数和节点发射功率相关的多目标优化问题， | The node access problem was formulated as a multi-objective optimization problem consi-dering link transmission rate, link handovers and transmit power. |
| 5147 | 利用对偶分解法将该问题分解为 2 个凸优化子问题：联合优化链路切换次数与链路传输速率和在保证链路可靠性的前提下优化节点发射功率。 | Then the optimization problem was decomposed into two convex optimization sub-problems by dual decomposition method, the sub-problem jointly optimizes handovers and link transmission rate while the sub-problem optimizes the transmit power based on link reliability. |
| 5148 | 仿真实验表明了所提出节点接入机制可有效地改善数据传递率、平均端到端时延以及网络吞吐量。 | Finally, simulationresults show that the proposed mechanism can effectively improve data delivery ratio, average end-to-end delay and net-work throughput. |
| 5149 | 基于云用户和云服务提供商的利益相关视角，以低计算成本（如能耗、经济成本和系统可用性等）满足云用户高 QoS 需求（如任务执行时间和任务最终完成时间），设计多维 QoS 云计算体系结构，构建多维 QoS 云资源调度模型， | A multidimensional cloud computing architecture is designed and a multidimensional cloud resource schedulingmodel is constructed based on the stakeholder perspective of cloud users and cloud service providers to meet the high QoSrequirements of cloud users (such as task execution time and task completion time) with low computing costs (such as ener-gy consumption, economic costs and system availability). |
| 5150 | 面向二级云资源调度，提出采用多重 Greedy 算法思想的 MQoS 云资源调度算法。 | For the second-level cloud resource scheduling, an MQoS cloudresource scheduling algorithm based on multiple Greedy algorithm is proposed. |
| 5151 | 实验结果表明，在具有无后效性的 4 种云计算应用场景下，MQoS 云资源调度算法相比 FIFO 云资源调度算法、M2EC 多维能耗虚拟机调度算法，其多维 QoS 度总体提升 206.42%~228.99%、34.26%~56.93%，其云数据中心负载均衡差平均总体降低 0.48~0.49、0.20~0.27。 | The experimental results show that under thefour cloud computing application scenarios with no aftereffects, the MQoS cloud resource scheduling algorithm has an over-all increase of 206.42%~228.99% and 34.26%~56.93 in terms of multidimensional QoS degree compared with FIFO andM2EC algorithms. It has an average overall reduction of 0.48~0.49 and 0.20~0.27 in terms of cloud data center load balancedifference. |
| 5152 | 针对车对车（V2V）通信系统无线传播场景的复杂性，基于几何随机信道建模方法，提出了一种改进的3D MIMO V2V 信道参考模型，依据方位角及仰角间确切的几何关系，导出了信道的空−时相关函数及空−多普勒功率谱密度，分析了信道相关特性影响因素。 | To match complex wireless propagation scenarios, an improved 3D geometry-based stochastic model wasproposed for vehicle to vehicle (V2V) communications channel. The exact relationship between the azimuth angle andelevation angle was taken into account and the corresponding space–time correlation function and space–Doppler powerspectral density were derived, and the influence of important factors was analyzed. |
| 5153 | 结果表明，信道相关性在非各向同性散射环境下与散射体分布、天线阵列角度密切相关，在各向同性散射环境下受天线阵列仰角影响，高车流密度场景信道空−时相关性明显低于低车流密度场景。 | The observations and conclusions show that correlation characteristics is closely related to distribution of the scatterers and the angle of the antenna array under the non-isotropic scattering environment and is affected by the elevation angle of the antenna array under the iso-tropic scattering environment. And the space-time correlation characteristics in high vehicular traffic density is signifi-cantly lower than that in low vehicular traffic density. |