Machine Learning Based Network Coverage Guidance System
This addresses network availability issues for mobile users and service providers, but appears incremental as it applies existing clustering methods to a new domain.
The paper tackles the problem of poor network connectivity by identifying regions with weak coverage using machine learning clustering algorithms, and provides feedback to service providers and customers, including a mobile app for navigation to better coverage areas.
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.