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
收藏
页数：7
相关论文
共 27 条
  • [1] An AI-based Model for Smart Control of High-Mobility Phased Arrays
    Bordbar, Arman
    Boccia, Luigi
    Catarinucci, Luca
    Amendola, Giandomenico
    Colella, Riccardo
    2023 53RD EUROPEAN MICROWAVE CONFERENCE, EUMC, 2023, : 516 - 519
  • [2] Coordinating Mobile Actors in Pervasive and Mobile Scenarios: An AI-based Approach
    de Leoni, Massimiliano
    Marrella, Andrea
    Mecella, Massimo
    Valentini, Stefano
    Sardina, Sebastian
    17TH IEEE INTERNATIONAL WORKSHOPS ON ENABLING TECHNOLOGIES: INFRASTRUCTURES FOR COLLABORATIVE ENTERPRISES, PROCEEDINGS, 2008, : 82 - +
  • [3] AI4AR: An AI-Based Mobile Application for the Automatic Generation of AR Contents
    Pierdicca, Roberto
    Paolanti, Marina
    Frontoni, Emanuele
    Baraldi, Lorenzo
    AUGMENTED REALITY, VIRTUAL REALITY, AND COMPUTER GRAPHICS, AVR 2020, PT I, 2020, 12242 : 273 - 288
  • [4] AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope
    Ashish Singh
    Suresh Chandra Satapathy
    Arnab Roy
    Adnan Gutub
    Arabian Journal for Science and Engineering, 2022, 47 : 9801 - 9831
  • [5] AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope
    Singh, Ashish
    Satapathy, Suresh Chandra
    Roy, Arnab
    Gutub, Adnan
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (08) : 9801 - 9831
  • [6] An AI-based partial explainable prediction of rubber concrete strength on mobile devices
    Jin, Xinxiang
    Yang, Xincong
    Jiang, Yuexin
    Li, Ye
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 427
  • [7] AI-based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies
    Zhang, Yuegian
    Kantarci, Burak
    2019 13TH IEEE INTERNATIONAL CONFERENCE ON SERVICE-ORIENTED SYSTEM ENGINEERING (SOSE) / 10TH INTERNATIONAL WORKSHOP ON JOINT CLOUD COMPUTING (JCC) / IEEE INTERNATIONAL WORKSHOP ON CLOUD COMPUTING IN ROBOTIC SYSTEMS (CCRS), 2019, : 17 - 26
  • [8] AI-Based Adherence Prediction for Patients: Leveraging a Mobile Application to Improve Clinical Trials
    Roy, Dooti
    Zhu, Zheng
    Guan, Lei
    Feng, Shaolei
    Daniels, Kristen
    Sand, Michael
    CNS SPECTRUMS, 2023, 28 (02) : 224 - 224
  • [9] Ai-based adherence prediction for patients: leveraging a mobile application to improve clinical trials
    Roy, D.
    Zhu, Z.
    Guan, L.
    Feng, S.
    Daniels, K.
    Sand, M.
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2021, 53 : S116 - S116
  • [10] AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change
    Fathi, Soheil
    Srinivasan, Ravi S.
    Kibert, Charles J.
    Steiner, Ruth L.
    Demirezen, Emre
    SUSTAINABILITY, 2020, 12 (08)
123