A Machine Learning-Based Framework for Efficient LTE Downlink Throughput

Mohammed, Nihal H.; Nashaat, Heba; Abdel-Mageid, Salah; Rizk, Rawia Y.

doi:10.1007/978-3-030-51920-9_10

Cited by 2 publications

(4 citation statements)

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“…Also, some of these columns describe information regarding user handover [11], [45], [55], which is outside of the scope of the analysis. A deeper investigation and further analyses of the collected dataset can be found in our previous work [7], [43]. Also, regarding missing values, all null values are compensated with the median value of the entire row.…”

Section: B Empirical Evaluation Of the Aml-ctp Frameworkmentioning

confidence: 93%

“…Sklearn's SelectKBest chooses the most important features to include in the learning process [42]. It also analyzes the correlation between features as in [7], [43]. The proposed AML-CTP framework normalizes the traffic load using Min-Max scaling to avoid significant data variance.…”

Section: A Phase 1: Data Preprocessingmentioning

confidence: 99%

“…Also, regarding missing values, all null values are compensated with the median value of the entire row. After calculating the feature score using Sklearn's SelectKBest [7], [43], the high-scored features are utilized to choose the final feature set. Therefore, the method that extracts a final set of 50 features will have the highest score.…”

Section: B Empirical Evaluation Of the Aml-ctp Frameworkmentioning

confidence: 99%

“…This final set is then used as the network traffic load prediction target. Each feature is further explained in detail in [7], [43]. Finally, normalization is applied to the selected dataset to avoid significant data variance.…”

Section: B Empirical Evaluation Of the Aml-ctp Frameworkmentioning

confidence: 99%

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Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques

Nashaat,

Mohammed,

Abdel-Mageid

et al. 2024

IEEE Access

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Estimating and analyzing traffic patterns become essential in managing Quality of Service (QoS) metrics while assessing internet data traffic in cellular networks. Cellular network planners frequently apply various approaches to predict network traffic. However, most existing studies focus on using the available local data to jointly build prediction models, facing data security challenges and time complexity, especially with multi-dimensional datasets. Therefore, this paper proposes a framework to handle traffic prediction with the considerable potential of Machine Learning (ML) algorithms. An Adaptive Machine Learning-based Cellular Traffic Prediction (AML-CTP) framework is presented to select a suitable ML algorithm for multi-dimensional datasets. Its objective is to streamline and speed up the selection of an appropriate model for predicting network traffic load. The framework employs two density-based clustering algorithms to categorize similar nearby traffic into various clusters, considering data similarity and convergence. Additionally, it assesses data quality and homogeneity by training models with data samples from each cluster to accurately determine the most suitable machine learning model. The optimal model is selected from four supervised predicting algorithms, reducing training time and hardware complexity. Two case studies from a popular telecommunication equipment corporation in Egypt are implemented using reallife cellular traffic with multi-dimensional features. The case studies show that the framework can help reduce the computational cost of training the model and reduce the risk of overfitting. The experimental results show that selecting the best prediction model training could save up to 85% of computational time compared to two state-of-the-art techniques while achieving an accuracy of 98.8%.

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Section: B Empirical Evaluation Of the Aml-ctp Frameworkmentioning

confidence: 93%