QoS Prediction for 5G Connected and Automated Driving

Κουσαρίδας, Απόστολος; Manjunath, Ramya Panthangi; Perdomo, Jose Mauricio; Zhou, Chan; Zielinski, Ernst; Schmitz, Steffen; Pfadler, Andreas

doi:10.1109/mcom.110.2100042

Cited by 33 publications

(13 citation statements)

References 1 publication

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“…Especially in the field of automotive for V2X communication, there are several works on predicting the QoS for the mobile network. In this research field, models for V2X communication based on LTE [69,70] as well as 5G [71,72] have been studied. The models applied for the QoS prediction range from Support Vector Machines, Decision Trees, and Random Forest [68][69][70][71] to DL models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-based architectures [69,73].…”

Section: Communication Modulesmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.

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Section: Communication Modulesmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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“…In [20], the authors try to predict the channel quality indicator (CQI) by the SNR using machine learning techniques on simulated data. Furthermore, the 5G network itself provides the network data analytics function (NWDAF) that could be directly used for QoS prediction, and delivering this information to a vehicle-to-everything (V2X) application, as outlined in [21].…”

Section: Related Workmentioning

confidence: 99%

An Open Mobile Communications Drive Test Data Set and Its Use for Machine Learning

Farthofer¹,

Herlich²,

Maier³

et al. 2022

IEEE Open J. Commun. Soc.

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The capability to provide guarantees for network metrics, such as latency, data rate, and reliability will be an important factor for widespread adoption of next generation mobile networks and hence, such metrics play a central role in standards for new wireless communication technologies. However, due to the inherently stochastic nature of mobile communications, any guarantees can only be of statistical nature and are highly dependent on the actual physical environment. To analyze the stochastic behavior, this paper presents a tool chain for measurement, collection, evaluation, and prediction of controlled mobile communications drive test data. We also publish the underlying data set of measurements covering two years' worth of highway traffic on a 25 km long section comprising 267 198 data points. We statistically evaluate the data set and validate it with a corresponding data set from another source. Applying machine learning to the data set illustrates possible use cases: Feed-forward neural networks to predict the data rate in five application scenarios, LIME to explain the behavior of the model, and an autoencoder to describe the interaction of five signal strength parameters. The data set and the tool chain show how machine learning can be applied to wireless networks and provide fellow researchers with the means to make further experiments.

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“…The PIR is foreseen to suddenly drop (e.g., from 0.5 s to 0.05 s), to remain stable for a duration of T F and then to increase back to its original value. In this example scenario, the future prediction of the PIR can either come from the network [3] or from a decentralized system [18]. The lower PIR value is finally translated into a minimum IVD, d m .…”

Section: Fuel Consumption Optimizationmentioning

confidence: 99%

“…The coordination between the vehicles is supported by vehicle-to-vehicle (V2V), or vehicleto-everything (V2X) communications more generally. Safetyrelated time-critical applications tend to be limited by the lower-bound quality of service (QoS)-measured with key performance indicators (KPIs) such as packet error rate (PER), latency, data rate and packet inter-reception time (PIR)-of their communications systems [3]. In HDPL, this limitation affects the IVD allowed for the trucks, and therefore on the achievable fuel saving.…”

Section: Introductionmentioning

confidence: 99%

Fuel Efficient High-Density Platooning Using Future Conditions Prediction

Jornod

Pfadler²,

Carreira

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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A promising application of cooperative driving is high-density platooning, which main goal is to reduce fuel consumption by driving with inter-vehicle distances below ten meters. The prediction of factors influencing the platoon capability to drive with such inter-vehicle distances the derived safe inter-vehicle distances, drives the potential fuel saving. Our aim is to study the influence of the prediction, especially the prediction horizon, on the achieved fuel saving as a function of different maneuver parameters. The contributions of this paper are: introducing the concept of maneuver reference to distribute the effort of maneuvering in truck platooning; linking the fuel consumption to a compensation time, that is the time during which the platoon will counterbalance the fuel consumption by benefiting from the reduced air drag; presenting an optimization method for maximizing the fuel saving depending on some predictive quality of service parameters. To model the fuel consumption and the duration of the maneuvers, we use a lasso regressions on data obtained from simulation. We then use these regression models in our optimization framework, which is based on particle swarm optimization. We show that to benefit from high-density platooning, the magnitude order of the prediction horizon required by a five-truck platoon is minimum hundred seconds.INDEX TERMS Intelligent vehicles, vehicular ad hoc networks, optimisation, cooperative systems.

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QoS Prediction for 5G Connected and Automated Driving

Cited by 33 publications

References 1 publication

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

An Open Mobile Communications Drive Test Data Set and Its Use for Machine Learning

Fuel Efficient High-Density Platooning Using Future Conditions Prediction

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