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Welcome to the Machine Learning-Based Predictive Modeling for 4G Long Term Evolution (LTE) Traffic Prediction project! This cutting-edge initiative harnesses the capabilities of machine learning to forecast 4G LTE traffic patterns, enabling more efficient network management and optimization.
The objective of this project is to build an advanced predictive modeling system for 4G Long Term Evolution (LTE) traffic prediction. By employing state-of-the-art machine learning technologies and data analysis, we aim to provide accurate forecasts of LTE traffic patterns, enhancing network efficiency and performance. This predictive modeling system will empower network operators with valuable insights for better resource allocation and network management. Smartphones are increasingly using artificial intelligence (AI), which is based on machine learning (ML). ML is also being used in the Edge paradigm of next-generation networks (NGNs) to predict and optimize network load, which is growing rapidly due to human traffic. Both standard and deep ML techniques are being used to improve NGN operation in complex heterogeneous environments. This paper proposes a method to predict traffic on the LTE network edge using the ML techniques Random Forest, Bagging, Support Vector Machines (SVMs), Bayes algorithm, Tweedie Regressor, RANSAC Regressor and Huber Regressor. We developed a corresponding ML environment based on a public cellular traffic dataset and compared the quality metrics and algorithm running time for each model. The SVM method allows the model to train much faster than the bagging and random forest algorithms. While bagging and random forest operate well with a mixture of numerical and categorical features, the SVM requires scaling the dataset and has difficulty finding nonlinear dependencies in the data.
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