Mobile Edge Computing-Based Real-Time English Translation With 5G-Driven Network Support

Liguo Wang, H. Yang
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Abstract

Real-time English Translation (RET) requires high network bandwidth and low network delay to provide better quality of experience, and even needs the support of massive connection to provide more services. For the three metrics, the traditional strategies are difficult to realize RET well. With the fast development of Mobile Edge Computing (MEC) and 5G network, the guarantee of three metrics has become very possible. Therefore, this paper studies MEC-based RET with 5G-driven network support, called 5GMR. On one hand, 5G-driven network has the natural properties to support high bandwidth, low delay and massive connection. On the other hand, MEC is used to offload the complex tasks related to the computation of English sentences into the edge server for the efficient computation, which not only saves energy consumption of mobile device but also decreases the whole network delay. In terms of the task scheduling in MEC, Genetic Algorithm (GA) is adopted to address it. The experimental results demonstrate that the proposed 5GMR is feasible and efficient.
基于移动边缘计算的实时英语翻译,支持5g驱动的网络
实时英语翻译(RET)需要高网络带宽和低网络延迟来提供更好的体验质量,甚至需要海量连接的支持来提供更多的服务。对于这三个指标,传统策略很难很好地实现RET。随着移动边缘计算(MEC)和5G网络的快速发展,三个指标的保证已经成为可能。因此,本文研究基于mec的RET与5g驱动的网络支持,称为5GMR。一方面,5g驱动的网络具有支持高带宽、低延迟和海量连接的天然属性。另一方面,MEC将英语句子计算相关的复杂任务卸载到边缘服务器进行高效计算,既节省了移动设备的能耗,又降低了整个网络的时延。针对MEC中的任务调度问题,采用遗传算法(Genetic Algorithm, GA)进行求解。实验结果证明了该方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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