Machine Learning Based Traffic Prediction System in Green Cellular Networks

Abstract

A network may be able to improve its capacity for planning and the quality of service, if it is possible to accurately estimate the volume of mobile phone traffic in advance it provides to customers. Even though there are currently an excessive number of mobile users connected to the network, the major objective is to enhance the network's quality and performance. It takes the idea of improving network performance and minimizes the amount of energy consumed, which can then be utilized to simplify and make consumers' lives easier by serving their needs as accurately as possible. This is done to alleviate the substantial traffic load that its use may cause on a cellular network. The energy consumption of the world's most powerful mobile networks is in the vicinity of gigawatts per year, making it one of the world's highest. In order to improve the service quality of a network, it is necessary to construct accurate and trustworthy time-series models for estimating the volume of cellular mobile traffic. This research project will permit the creation of a model that will aid in the intelligent prediction of cellular network load traffic. To counteract the enormous traffic load that a cellular network is now experiencing, the Quality of service should be simplified and improved. This will facilitate meeting user needs as exactly as possible. It is determined where the users are, what the signal-to-interference-and-noise ratio is, and what the users' quality of service expectations are, and the transmit power is then adjusted to the least amount necessary to meet those needs. There will never be a day when all base stations are completely in switched off state. Adjustments are made to the transmit power to ensure it is set at the lowest possible level, taking into consideration the amount of signal-to-noise ratio and the users' quality of service requirements.