Modeling and Prediction LTE 4G NW according to Memory Algorithm of Long-Short Term


  • Keyan Abdul Aziz Mutlaq University of Basrah, IT and Communication center
  • Hassanain Raheem Kareem Physics Department, College of Education, Misan University
  • Ghida Yousif Abbass University of Basrah, IT and Communication center


Long-Short Term Memory, Deep learning, 4G NW, time series model, Prediction NW


With increasing of using smart phone, has run to a tense growing in internet traffic.  Therefore, prediction and modeling NW turn to be very significant for monitoring NW for increasing quality of services (QoS). The novelty of designed prediction model for managing the NW intelligently.  In this paper, proposed   Long-Short Term Memory (LSTM) to forecast traffic as cellular. The normalization as min-max method has been implemented for scaling the NW traffic NT data.  The LSTM model was evaluated by employing real standard LET NW loading that gathered from kaggel.   The empirical results of LSTM model have revealed that has achieved greater accuracy, according to R metrics 98.67% at the training phase. The prediction NT was very close to the target values, this is approved the robustness of the deep learning model LSTM for handling LET traffic. Where the proposed system at unseen data (testing phase) has achieved superior performance, the correlation percentage of the LSTM model at testing phase is 97.95%. Finally, we believe that the system has ability to monitoring LET NW.


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How to Cite

K. A. . Aziz Mutlaq, H. R. . Kareem, and G. Y. . Abbass, “Modeling and Prediction LTE 4G NW according to Memory Algorithm of Long-Short Term ”, Int J Intell Syst Appl Eng, vol. 11, no. 1s, pp. 172–178, Jan. 2023.