Cellular Traffic Prediction with Recurrent Neural Network

Abstract

Autonomous prediction of traffic demand will be a key function in future cellular networks. In the past, researchers have used statistical methods such as Autoregressive integrated moving average (ARIMA) to provide traffic predictions. However, ARIMA based predictions fail to give an exact and accurate forecast for dynamic input quantities such as cellular traffic. More recently, researchers have started to explore deep learning techniques, such as, recurrent neural networks (RNN) and longshort-term-memory (LSTM) to autonomously predict future cellular traffic. In this research, we have designed a LSTM based cellular traffic prediction model. We have compared the LSTM based prediction with the base line ARIMA model and vanilla feed-forward neural network (FFNN). The results show that LSTM and FFNN accurately predicted the future cellular traffic. However, it was found that LSTM train the prediction model in much shorter time as compared to FFNN. Hence, we conclude that LSTM models can be effectively even used with small amount of training data which will allow to timely predict the future cellular traffic.

From statistical‐ to machine learning‐based network traffic prediction

“…42 Thanks to the Activate Function, ANNs can learn complex nonlinear dependencies among numerous variables, thus they are generally known as Universal Function Approximators. 42 The general architecture of ANNs is a directed graph 43 consisting of input and output layers, which are connected via the so-called hidden layer, which itself could be consist of one or more layers. Input values reach the output layer by applying transformations through the hidden layers.…”

“…Jaffry 43 introduces a LSTM-based model for traffic prediction in the Long Term Evolution-Advanced (LTE-A) network. It has been compared with similar paradigms based on ARIMA and FFNN in terms of performance and accuracy.…”