Machine Learning Based Network Coverage Guidance System

• URL: http://arxiv.org/abs/2010.13190v1
• Date: Sun, 25 Oct 2020 19:01:16 GMT
• Title: Machine Learning Based Network Coverage Guidance System
• Authors: Srikanth Chandar, Muvazima Mansoor, Mohina Ahmadi, Hrishikesh Badve, Deepesh Sahoo, Bharath Katragadda
• Abstract summary: With the advent of 4G, there has been a huge consumption of data and the availability of mobile networks has become paramount. In this paper, we introduce a novel approach, to identify the regions that have poor network connectivity. The solution enables customers to navigate to a better mobile network coverage area with stronger signal strength location.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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