ID	原文	译文
4924	首先按照网络的分层体系架构对时延来源进行分析，并以此为基础对降低时延的技术进行了综述。	The sources of latency according to the layered architecture of the network was analyzed, and summarizes the techniques for reducingthe latency.
4925	然后，针对数据中心网络、5G 以及边缘计算 3 种典型的低时延关键场景及优化时延的技术进行分析。	After that, three typical low-latency key scenarios and delay optimization techniques for data center network,5G and edge computing was analyzed.
4926	最后，从网络架构革新、数据驱动优化时延算法及新协议设计 3 个方面展望了低时延网络发展面临的机遇与挑战。	Finally, the opportunities and challenges that may be encountered in the develop-ment of low latency networks were presented from the perspectives of network architecture innovation, data-driven la-tency optimization algorithm and the design of new protocols.
4927	针对第五代移动通信技术（5G）及后 5G 部署之后对互联网主干产生的影响开展定性和定量研究。	The effects that the 5th generation mobile network (5G) bring to Internet backbone were investigated qualita-tively and quantitatively.
4928	首先分析了 5G 所具有的超大带宽、超低时延和海量机器连接的特性对互联网主干在流量、时延、安全等方面带来的挑战，	First, the challenges that the characteristics of 5G, i.e. ultra-high data rate, ultra-low latency, andultra-large number of connections, introduce to Internet backbone in terms of traffic, latency, and security were analyzed.
4929	然后建立了抽象模型用于描述 5G 用户获取内容和服务的特性，以及 5G、边缘计算、云计算相结合场景下主干网流量的特征。	Second, a model was proposed to capture the characteristics of 5G users and Internet traffic with the coordination of 5G,edge computing, and cloud computing.
4930	以此为基础，开展了数值模拟实验，评价了在不同程度的 5G 部署场景下，互联网主干网性能和用户体验到的带宽和时延等服务质量指标。	Then, numerical simulations were used to evaluate the model. The QoS require-ments that Internet backbone faces under different extent of 5G deployment were evaluated.
4931	研究表明，5G 的部署会引起互联网主干流量增加，端到端时延中传播时延所占比例增大，以及带宽瓶颈由接入网转向主干网等结果。	According to the study, in-crement of backbone traffic, increment of the ratio of propagation delay, and movement of bandwidth bottleneck are pre-dicted after 5G/B5G deployment.
4932	针对现有异构云无线接入网络的研究主要集中在单个蜂窝网络场景，仅考虑了蜂窝网络内部干扰，忽略了蜂窝网络间干扰的问题，研究了多个蜂窝网络共存场景下的 H-CRAN，通过最大化系统总传输速率，对宏基站和无线远端射频单元的波束成形向量进行联合优化。	To overcome the problem that previous researches for heterogeneous cloud radio access network (H-CRAN)mainly focus on single macro cell, and only considered the intracell interference in the one macro cell, while the inter cellinterferences among different macro cells are neglected, H-CRAN with multiple macro-cells was studied, and the objec-tive was to maximize system sum-rate through jointly optimizing the beamforming vectors of macro base stations (MBS)and remote radio heads (RRH).
4933	基于交替优化算法和拉格朗日对偶方法，提出了一种 MBS和 RRH 波束成形向量联合优化算法。	Based on alternating optimization and Lagrangian dual method, a joint MBS and RRHbeamforming algorithm was proposed.