Cellular Traffic Prediction Based on an Intelligent Model

Abstract

The evolution of cellular technology development has led to explosive growth in cellular network traffic. Accurate time-series models to predict cellular mobile traffic have become very important for increasing the quality of service (QoS) with a network. The modelling and forecasting of cellular network loading play an important role in achieving the greatest favourable resource allocation by convenient bandwidth provisioning and simultaneously preserve the highest network utilization. The novelty of the proposed research is to develop a model that can help intelligently predict load traffic in a cellular network. In this paper, a model that combines single-exponential smoothing with long short-term memory (SES-LSTM) is proposed to predict cellular traffic. A min-max normalization model was used to scale the network loading. The single-exponential smoothing method was applied to adjust the volumes of network traffic, due to network traffic being very complex and having different forms. The output from a single-exponential model was processed by using an LSTM model to predict the network load. The intelligent system was evaluated by using real cellular network traffic that had been collected in a kaggle dataset. The results of the experiment revealed that the proposed method had superior accuracy, achieving R-square metric values of 88.21%, 92.20%, and 89.81% for three one-month time intervals, respectively. It was observed that the prediction values were very close to the observations. A comparison of the prediction results between the existing LSTM model and our proposed system is presented. The proposed system achieved superior performance for predicting cellular network traffic.