Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting

Mohamed Khalafalla Hassan (University Technology Malaysia)
Sharifah Hafizah Sayed Ariffin (University Technology Malaysia)
Sharifah Kamilah Syed-Yusof (University Technology Malaysia)
Nurzal Effiyana Ghazali (University Technology Malaysia)
Mohammed Eltayeb Ahmed Kanona (Future University)
Mohamed Rava (University Technology Malaysia)

International Journal of Electrical and Computer Engineering
Vol 12, No 6: December 2022

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.