QoS Evaluation and Prediction for C-V2X Communication in Commercially-Deployed LTE and Mobile Edge Networks

Torres-Figueroa, Luis; Schepker, Henning; Jiru, Josef

doi:10.1109/vtc2020-spring48590.2020.9129382

Cited by 19 publications

(8 citation statements)

References 9 publications

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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%

“…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]. The related work presented include model deployment [70] and QoS prediction in the UAV context [73].…”

Section: Communication Modulesmentioning

confidence: 99%

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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%

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 a previous study [10], the authors created models for end-to-end delay prediction in Cellular Vehicle-to-Everything (C-V2X) communication using different ML techniques.…”

Section: Review Of Relevant Published Researchmentioning

confidence: 99%

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

Stojčić,

Banjanin,

Vasiljević

et al. 2023

Applied Sciences

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Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

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“…Subsequently, all the QoS parameters results such as throughput and end-to-end latency from the direct communication between transmitter−receiver will be used for a recursive feedback loop in a real−time QoS−based ML prediction model. This predictive model will also be installed in a real-time mobile edge computing (MEC) development in an urban scenario [1]. Thus, in this case, MCS will select dynamically not only the modulation specifications but also the channel coding conditions.…”

Section: Gpp 5g V2x Qos Scenarios and Proposed Dynamic System Modelmentioning

confidence: 99%

“…Cellular vehicle-to-everything (C-V2X) communication represents the dominant technology for future cooperative automated driving and safety-related applications. The requirements in terms of QoS performance vary according to specific user cases that represent realistic 3GPP scenarios [1]. References [2,3] provide an overview on V2X standardization on the New Radio (NR) side−link design as part of 3GPP New Radio (NR) Release 16, which improves network architecture, security, physical layer and protocol aspects considering the reliability and low latency requirements.…”

Section: Introduction and Related Workmentioning

confidence: 99%

3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

Kosmanos

Chaikalis

Savvas

2022

Telecom

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Cellular vehicle-to-everything (C-V2X) communication has recently gained attention in industry and academia. Different implementation scenarios have been derived by the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) Vehicle-to-Everything (V2X) standard, Release 16. Quality of service (QoS) is important to achieve reliable communication and parameters which have to be considered are reliability, end-to-end latency, data rate, communication range, throughput and vehicle density for an urban area. However, it would be desirable to design a dynamic selecting system (with emphasis on channel coding parameters selection) so that all QoS parameters are satisfied. Having this idea in mind, in this work we examine nine V2X implementation scenarios using Long Term Evolution (LTE) turbo coding with a geometry-based efficient propagation model for vehicle-to-vehicle communication (GEMV), where we consider the above QoS parameters for SOVA, log-MAP and max-log-MAP decoding algorithms. Our study is suitable for 3GPP cooperative sensing, for the eight scenarios considering medium and large signal-noise-ratio (SNR) values. The proposed model is sustainable despite a doubled data rate, which results in a minimal bit error rate (BER) performance loss up to 1.85 dB. In this case tripling the data rate results in a further 1 dB loss. Moreover, a small loss up to 0.4 dB is seen for a vehicle speed increase from 60 km/h to 100 km/h. Finally, increasing vehicle density has no effect on the implemented 3GPP scenario considering end-to-end latency, irrespectively from the decoding algorithm.

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QoS Evaluation and Prediction for C-V2X Communication in Commercially-Deployed LTE and Mobile Edge Networks

Cited by 19 publications

References 9 publications

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

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

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