Open AccessA mobile traffic load prediction based on recurrent neural network: A case of telecommunication in Afghanistan
Author(s) -
Ahmadzai Fazel Haq,
Lee Woongsup
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12534
Subject(s) - computer science , artificial neural network , base station , recurrent neural network , real time computing , operator (biology) , mobile telephony , scheme (mathematics) , simple (philosophy) , computer network , data mining , artificial intelligence , telecommunications , mobile radio , mathematics , mathematical analysis , biochemistry , chemistry , philosophy , epistemology , repressor , transcription factor , gene
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.