Abstract

The Mobile networks have become the backbone of telecommunications in recent years, with the widespread use of cell phones, tablets and other mobile devices. Networks are technologies that are constantly evolving, used to interact with and connect with consumers. The frequencies of mobile networks can be used by multiple network subscribers at the same time. The Cell tower sites and mobile devices handle frequencies so that low-power interrupters can minimize their services. Signal reception, call quality, and speed depend on many factors. The User location, service provider, and equipment all play a role. A mobile network is a complex spider web that includes communication towers, antennas, network cores, and devices that generate traffic generating end-to-end data flow across our mobile devices. In this paper, a smart machine learning network algorithm is established at the point of cells placed in a given spatial area, filled with rotation or transfer stations placed at the center of the cells. It beyond this basic framework, there are many different types of mobile networks are evaluated and achieved the best results as per the proposed machine learning method.