Effect of Spatial, Temporal and Network Features on Uplink and Downlink Throughput Prediction

Palaios, Alexandros; Vielhaus, Christian; Kulzer, Daniel Fabian; Geuer, Philipp; Sattiraju, Raja; Fink, Jochen; Kasparick, Martin; Watermann, Cara; Fettweis, Gerhard; Fitzek, Frank H. P.; Schotten, Hans D.; Stańczak, Sławomir

doi:10.1109/5gwf52925.2021.00080

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…There are plenty of use cases to apply XAI in communication networks [238]. These use cases include network planning and engineering [239], resource allocation [240], [241], performance management [128], [242], and security management [243], [244]. Most of these works use the methods presented in this chapter to make their models explainable.…”

Section: Explainable Artificial Intelligencementioning

confidence: 99%

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Afifi,

Pochaba,

Boltres

et al. 2024

IEEE Access

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Section: Explainable Artificial Intelligencementioning

confidence: 99%

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Afifi,

Pochaba,

Boltres

et al. 2024

IEEE Access

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“…Based on this, a long short-term memory (LSTM), trained with corresponding data, is selected for the throughput prediction. Some preliminary results for uplink (UL) and DL throughput prediction using Random Forest and employing the dataset used for this paper are presented in [34]. QoS prediction in a 5G non-standalone network is examined in [35].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

et al. 2023

Self Cite

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As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety-and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or highdefinition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.INDEX TERMS Intelligent transportation systems, machine learning, quality of service, throughput prediction, vehicular communication, wireless networks.

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“…In [13], the authors presented several machine learning and deep learning models for throughput prediction, which is crucial for delay reduction in online streaming services. Palaios et al in the paper [14] investigate the influence of spatial, temporal and network characteristics on the prediction of throughput in the uplink and downlink direction.…”

Section: Scientific Background Of the Researchmentioning

confidence: 99%

Prediction of User Throughput in the Mobile Network Along the Motorway and Trunk Road

Ćurguz¹,

Banjanin²,

Stojčić³

2022

Sci. Eng. Technol.

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The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the research is limited to the section of Motorway "9th January" (M9J) Banja Luka - Doboj, between the node Johovac and the town of Prnjavor (P-J section), and the area of the section of trunk road M17, between the node Johovac and the town of Doboj (J-D section). Based on the set of collected data, several models based on machine learning techniques were trained and tested together with the application of the Correlation-based Feature Selection (CFS) method to reduce the space of input variables. The test results showed that the models based on k-Nearest Neighbors (k-NN) have the lowest relative prediction error, for both sections, while the model created for the trunk road section has significantly better performance.

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Effect of Spatial, Temporal and Network Features on Uplink and Downlink Throughput Prediction

Cited by 9 publications

References 13 publications

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

Prediction of User Throughput in the Mobile Network Along the Motorway and Trunk Road

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