LTE downlink throughput modeling using neural networks

T. Rehman, M. I. Baig, Armaghan Ahmad
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引用次数: 8

Abstract

With the advancements made in the field of telecommunication, the quality of service is increasing day by day whilst the user load is simultaneously increasing on the service providers. In order to keep up with the tougher standards, a fairly large amount of money is spend on resource allocation, majority of which often ends up unused and as a result end up being wasted. In this paper we used LTE data obtained on hourly basis for a period of 60 days from a Telecommunication company and carried out its quantitative analysis using deep neural nets. The results have shown stark difference in utilization of resources across rural and urban areas. Also we were able to obtain a handful of key features, which play major role in determining the quality of data transmission, and emphasis on theses could ensure better quality at lesser cost.
基于神经网络的LTE下行链路吞吐量建模
随着通信技术的不断进步,服务质量日益提高,同时用户对服务提供商的负荷也在不断增加。为了跟上更严格的标准,在资源配置上花费了相当大的一笔钱,其中大部分往往没有被使用,结果被浪费了。在本文中,我们使用从一家电信公司获得的60天内每小时的LTE数据,并使用深度神经网络进行定量分析。调查结果显示,农村和城市地区在资源利用方面存在明显差异。此外,我们还能够获得一些关键特性,这些特性在决定数据传输质量方面起着重要作用,并且强调这些特性可以确保以更低的成本获得更好的质量。
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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