Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM

Stepanov, Nikolai A.; Alekseeva, Daria; Ometov, Aleksandr; Lohan, Elena Simona

doi:10.1109/icumt51630.2020.9222418

Cited by 25 publications

(17 citation statements)

References 14 publications

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“…A new method for LTE network traffic prediction is presented in [19]. They used three different machine learning algorithms, including the RF, Bagging, and SVM on public cellular traffic datasets with the aim of network traffic prediction.…”

Section: A Network Traffic Predictionmentioning

confidence: 99%

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sepasgozar

Pierre

2022

IEEE Access

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Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic by considering the parameters that can lead to road traffic happening. The proposed model integrates a Random Forest-Gated Recurrent Unit-Network Traffic Prediction algorithm (RF-GRU-NTP) to predict the network traffic flow based on the traffic in the road and network simultaneously. This model has three phases including network traffic prediction based on V2R communication, road traffic prediction based on V2V communication, and network traffic prediction considering road traffic happening based on V2V and V2R communication. The hybrid proposed model which implements in the third phase, selects the important features from the combined dataset (including V2V and V2R communications), by using the Random Forest (RF) machine learning algorithm, then the deep learning algorithms to predict the network traffic flow apply, where the Gated Recurrent Unit (GRU) algorithm gives the best results. The simulation results show that the proposed RF-GRU-NTP model has better performance in execution time and prediction errors than other algorithms which used for network traffic prediction.

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Section: A Network Traffic Predictionmentioning

confidence: 99%

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sepasgozar

Pierre

2022

IEEE Access

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“…Subsequently, the classification is performed according to the halfspace of each sample. SVM can also be used in regression and network traffic prediction problems, as in Stepanov et al [19]. The authors compared SVM forecasting results with predictions calculated by using the Random Forecast algorithm.…”

Section: B Data-driven Approachesmentioning

confidence: 99%

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

et al. 2023

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This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

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“…Each tree selects a particular prediction and the random forest then averages the different predictions provided by the decision trees, which improves prediction accuracy. The random forest has been studied extensively in the literature (see [52][53][54][55]). Its architecture is shown in Figure 4.…”

Section: Random Forestmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

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Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM

Cited by 25 publications

References 14 publications

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

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