A Deep Learning Approach for Location Independent Throughput Prediction

Schmid, Josef; Schneider, Mathias; Hob, Alfred; Schuller, Björn

doi:10.1109/iccve45908.2019.8965216

Cited by 22 publications

(8 citation statements)

References 11 publications

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“…In [31] and [32] the authors present a measurement campaign and evaluate several ML models regarding their performance for Downlink (DL) throughput prediction. However, the authors measure in a public network and thus cannot consider features such as the cell load.…”

Section: State Of the Artmentioning

confidence: 99%

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Palaios¹,

Vielhaus²,

Kulzer³

et al. 2023

Preprint

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As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML 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 (QoS) requirement. With predictive Quality of Service (pQoS), 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 HD mapping applications. We discuss the entire ML 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 ML models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on ML 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 ML models within diverse scenarios. We also use Explainable AI (XAI) to show that ML 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.

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Section: State Of the Artmentioning

confidence: 99%

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Palaios¹,

Vielhaus²,

Kulzer³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, the RF model was created in [7] to predict Video Streaming throughput. As a result of research in [8], different models were created based on Deep Neural Network (DNN) approaches, which enable throughput prediction in areas where there is no previous data on mobile network performance. When using a Live Streaming service, especially at high vehicle speeds, frequent fluctuations of Uplink connection throughput occur, which causes service delays.…”

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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“…In order to increase the performance and reliability of the UAV to server connection, QoS modeling and prediction need to be investigated in future work. Thereby, the experience in QoS prediction gained by previous projects, regarding vehicle to server communication in the automotive sector, will be used to accomplish these enhancements [22], [23].…”

Section: Extension and Coordinated Data And Control Transmissionmentioning

confidence: 99%

System Requirements Specification for Unmanned Aerial Vehicle (UAV) to Server Communication

Purucker

Schmid

Hob

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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Within this paper, requirements for server to Unmanned Aerial Vehicle (UAV) communication over the mobile network are evaluated. It is examined, whether a reliable cellular network communication can be accomplished with the use of current Long Term Evolution (LTE) network technologies, or, if the 5th Generation (5G) network is indispensable. Moreover, enhancements on improving the channel quality on the UAVside are evaluated. Therefore, parameters like data rate, latency, message size and reliability for Command and Control (C&C) and application data are determined. Furthermore, possible improvements regarding interference mitigation in the up-and downlink of the UAV are discussed. For this purpose, results from publications of the 3rd Generation Partnership Project (3GPP) and from surveys regarding UAVs and mobile networks are presented. This work shows that, for C&C use cases like steering to waypoints, the latency and the data rate of the LTE network is sufficient, but in terms of reliability problems can occur. Furthermore, the usability of standard protocols for computer networks like the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) is discussed. There are also multimodal implementations of these protocols like MultiPath TCP (MPTCP) which can be adapted into the UAV's communication system in order to increase reliability through multiple communication channels. Finally, applications for Long Range (LoRa) direct communication in terms of supporting the cellular network of the UAV are considered.

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A Deep Learning Approach for Location Independent Throughput Prediction

Cited by 22 publications

References 11 publications

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

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

System Requirements Specification for Unmanned Aerial Vehicle (UAV) to Server Communication

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