论文标题

基于机器学习的网络覆盖指导系统

Machine Learning Based Network Coverage Guidance System

论文作者

Chandar, Srikanth, Mansoor, Muvazima, Ahmadi, Mohina, Badve, Hrishikesh, Sahoo, Deepesh, Katragadda, Bharath

论文摘要

随着4G的出现,数据消耗大量消耗,移动网络的可用性变得至关重要。同样,随着基于用户消费的网络流量爆发,数据可用性和网络异常已大大增加。在本文中,我们介绍了一种新颖的方法,以确定网络连通性较差的地区,从而向服务提供商提供反馈,以改善覆盖范围以及向客户明智地选择网络。除此之外,该解决方案使客户能够使用机器学习聚类算法导航到具有更强信号强度位置的更好的移动网络覆盖区域,同时将其部署为移动应用程序。它还提供了在附近地理区域之间各个网络强度和范围不同的动态视觉表示。

With the advent of 4G, there has been a huge consumption of data and the availability of mobile networks has become paramount. Also, with the burst of network traffic based on user consumption, data availability and network anomalies have increased substantially. In this paper, we introduce a novel approach, to identify the regions that have poor network connectivity thereby providing feedback to both the service providers to improve the coverage as well as to the customers to choose the network judiciously. In addition to this, the solution enables customers to navigate to a better mobile network coverage area with stronger signal strength location using Machine Learning Clustering Algorithms, whilst deploying it as a Mobile Application. It also provides a dynamic visual representation of varying network strength and range across nearby geographical areas.

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