Machine Learning Based Network Coverage Guidance System

TL;DR

This paper presents a machine learning-based system that identifies poor network coverage areas and guides users to better signal zones, aiding service providers and customers with dynamic visual coverage maps.

Contribution

It introduces a novel machine learning clustering approach for real-time network coverage guidance integrated into a mobile application.

Findings

Effective identification of low coverage regions.

Real-time navigation to stronger signal areas.

Dynamic visual coverage representation.

Abstract

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.