AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios

被引：5

作者：

Kuelzer, Daniel F. ^{[1
]}

Kasparick, Martin ^{[2
]}

Palaios, Alexandros ^{[3
]}

Sattiraju, Raja ^{[5
]}

Ramos-Canto, Oscar D. ^{[4
]}

Wieruch, Dennis ^{[2
]}

Tchouanken, Hugues ^{[4
]}

Goettsch, Fabian ^{[6
]}

Geuer, Philipp ^{[3
]}

Schwardman, Jens ^{[4
]}

Fettweis, Gerhard ^{[6
]}

Schotten, Hans D. ^{[5
]}

Stanczak, Slawomir ^{[2
,7
]}

机构：

[1] BMW Grp Res New Technol Innovat, Munich, Germany

[2] Fraunhofer Heinrich Hertz Inst, Berlin, Germany

[3] Ericsson Res, Dusseldorf, Germany

[4] Robert Bosch GmbH, Gerlingen, Germany

[5] Univ Kaiserslautern, Kaiserslautern, Germany

[6] Tech Univ Dresden, Dresden, Germany

[7] Tech Univ Berlin, Berlin, Germany

来源：

2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING) | 2021年

关键词：

WIRELESS NETWORKS; MACHINE; DESIGN;

D O I：

10.1109/VTC2021-Spring51267.2021.9449059

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.

引用

页数：7

共 27 条

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