Cellular network traffic has grown rapidly as a result of the development of cellular technology. In order to achieve the most advantageous resource allocation through practical bandwidth provisioning and maintain the maximum network utilization, modelling and forecasting of cellular network loading are crucial. The goal of this is to create a model that can aid in the intelligent prediction of load traffic onto the cellular network. In this study, the model for predicting cellular traffic is developed that incorporates Transverse LSTM, PCA, and Discrete Wavelet. The main goal is to design a greener and traffic-friendly 5G/IMT-2020 network (SDN/NFV) with efficient resource allocation to ensure good quality of service.

Keywords : Prediction, Wireless mesh networks, Deep learning, Machine learning