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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mohamed Khalafalla Hassan
University Technology Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast Mohamed Khalafalla Hassan; Sharifah H. S. Ariffin; Sharifah Kamilah Syed- Yusof; N. Effiyana Ghazali; Mohamed EA Kanona
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.17024

Abstract

The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests.
Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting Mohamed Khalafalla Hassan; Sharifah Hafizah Sayed Ariffin; Sharifah Kamilah Syed-Yusof; Nurzal Effiyana Ghazali; Mohammed Eltayeb Ahmed Kanona; Mohamed Rava
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6859-6868

Abstract

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.
Co-Authors Mohamed EA Kanona Mohamed Rava Mohammed Eltayeb Ahmed Kanona N. Effiyana Ghazali Nurzal Effiyana Ghazali Sharifah H. S. Ariffin Sharifah Hafizah Sayed Ariffin Sharifah Kamilah Syed- Yusof Sharifah Kamilah Syed-Yusof