A mobile traffic load prediction based on recurrent neural network: A case of telecommunication in Afghanistan

Abstract

This paper investigates the prediction of mobile traffic load based on four variants of recurrent neural networks, which are the simple long short-term memory (LSTM), stacked LSTM, gated recurrent unit (GRU) and bidirectional LSTM. In the considered schemes, the mobile traffic load of 15 min ahead of time is estimated based on the previous mobile traffic load data. The performance of the proposed scheme is verified using realistic traffic load data collected from the base station located in Kabul city, Afghanistan, which belongs to the SALAAM telecommunication operator during December 2020 and January 2021. Through performance evaluation, the authors confirm that the traffic load can be predicted with high accuracy using considered schemes and the GRU-based scheme outperforms other schemes in terms of accuracy.