Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps fo...

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Bibliographic Details
Main Authors: Ariffin, S.H.S (Author), Ghazali, N.E (Author), Hamad, M. (Author), Hamam, H. (Author), Hamdan, M. (Author), Hamdi, M. (Author), Hassan, M.K (Author), Khan, S. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
5G mobile communication systems
Block-chain
dynamic learning
Dynamic learning
Forecasting
Learning frameworks
local smoothing
Local smoothing
Long short-term memory
Long Term Evolution (LTE)
LSTM
Machine-learning
New applications
New services
Quality of service
Resource allocation
slice
Slice
Time series
Times series
traffic forecast
Traffic forecasts
Vehicle to Everything
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