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

: Torres-Figueroa, Luis; Schepker, Henning F.; Jiru, Josef

Postprint urn:nbn:de:0011-n-5935281 (3.4 MByte PDF)
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Created on: 8.7.2020

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 91st Vehicular Technology Conference, VTC2020-Spring. Proceedings : 25-28 May 2020, Antwerp, Belgium, virtual event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-5207-3
ISBN: 978-1-7281-4053-7
ISBN: 978-1-7281-5206-6
7 pp.
Vehicular Technology Conference (VTC Spring) <91, 2020, Online>
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie StMWi
0703/685 69/24/15/48/16/49/17/50/18/
Vernetzte Mobilität – Intelligente Fahrzeugvernetzung
Conference Paper, Electronic Publication
Fraunhofer IKS ()
cellular vehicle-to-everything; C-V2X communication; OpenAirInterface; Long Term Evolution; LTE; mobile edge computing; MEC; quality of service; QoS; QoS prediction; machine learning; Software Defined Radio; SDR

Cellular vehicle-to-everything (C-V2X) communication is a key enabler for future cooperative automated driving and safety-related applications. The requirements they demand in terms of Quality of Service (QoS) performance vary according to the use case. For instance, Day-1 applications such as Emergency Brake Light warning have less strict requirements than remote driving. In this paper, we seek to answer two questions: Are current LTE networks ready to support Day-1 applications at all times? And, can underperforming situations be reliably predicted based on GPS and network-related information? To address these questions, we first implement a system that collects positioning data and LTE key performance indicators (KPIs) with a higher time resolution than commercial off-the-shelf LTE modems, while simultaneously measuring the end-to-end (E2E) delay of an LTE network. We then use this system to assess the readiness of multiple mobile network operators (MNOs) and a live Mobile Edge Computing (MEC) deployment in an urban scenario. For evaluating whether an adaptable operation is possible in adverse circumstances, e.g., by performing hybrid networking or graceful degradation, we finally use Machine Learning to generate a client-based QoS predictor and forecast the achievable QoS levels.