Predictive Quality of Service in Cellular Networks: Challenges, Framework, and Application in Vehicular Communications

被引：2

作者：

Blasco, Ricardo ^{[1
]}

Ferrante, Guido Carlo ^{[1
]}

Watermann, Cara ^{[1
]}

Palaios, Alexandros ^{[2
]}

机构：

[1] Ericsson Res, Stockholm, Sweden

[2] Rhein Westfal TH Aachen, Aachen, Germany

来源：

IEEE COMMUNICATIONS MAGAZINE | 2023年 / 61卷 / 03期

关键词：

Quality of service; Uncertainty; Data collection; Predictive models; Cellular networks; Behavioral sciences; Radio access networks; MACHINE;

D O I：

10.1109/MCOM.004.2200180

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：

Predictive quality of service will allow next-generation cellular networks to improve the management of the existing services and support more demanding use cases. In this article, we discuss the main challenges on the way to practical realizations of predictive quality of service, from fundamental considerations related to prediction horizons and handling uncertainty to practical aspects on data collection and implications of the architectural choice. We also present a general methodology for predictive quality of service that addresses some of the main challenges and may be used to realize predictive quality of service at scale. As a case study, we apply the framework to a vehicular communications setting using a data-set acquired from a deployed private LTE network.

引用

页码：44 / 49

页数：6

共 15 条

[1]

5G-ACIA, 2019, WHIT PAP 5G AUT IND

[2]

5GAA, 2019, WHIT PAP MAK 5G PROA

[3] Predictive Quality of Service: The Next Frontier for Fully Autonomous Systems [J].

Boban, Mate ;

Giordani, Marco ;

Zorzi, Michele .

IEEE NETWORK, 2021, 35 (06) :104-110

[4]

Jomrich F., 2018, P 4 INT C VEH TECHN, P121, DOI DOI 10.5220/0006692501210132

[5]

Kelleher JD, 2015, FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS: ALGORITHMS, WORKED EXAMPLES, AND CASE STUDIES, P1

[6] A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction [J].

Kougioumtzidis, Georgios ;

Poulkov, Vladimir ;

Zaharis, Zaharias D. ;

Lazaridis, Pavlos, I .

IEEE ACCESS, 2022, 10 :19507-19538

[7] QoS Prediction for 5G Connected and Automated Driving [J].

Kousaridas, Apostolos ;

Manjunath, Ramya P. ;

Perdomo, Jose ;

Zhou, Chan ;

Zielinski, Ernst ;

Schmitz, Steffen ;

Pfadler, Andreas .

IEEE COMMUNICATIONS MAGAZINE, 2021, 59 (09) :58-64

[8] A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario [J].

Mason, Federico ;

Drago, Matteo ;

Zugno, Tommaso ;

Giordani, Marco ;

Boban, Mate ;

Zorzi, Michele .

2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2022, :114-119

[9] Effect of Spatial, Temporal and Network Features on Uplink and Downlink Throughput Prediction [J].

Palaios, Alexandros ;

Vielhaus, Christian ;

Kuelzer, Daniel F. ;

Geuer, Philipp ;

Sattiraju, Raja ;

Fink, Jochen ;

Kasparick, Martin ;

Watermann, Cara ;

Fettweis, Gerhard ;

Fitzek, Frank H. P. ;

Schotten, Hans D. ;

Stanczak, Slawomir .

2021 IEEE 4TH 5G WORLD FORUM (5GWF 2021), 2021, :418-423

[10] Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications [J].

Park, Jihong ;

Samarakoon, Sumudu ;

Elgabli, Anis ;

Kim, Joongheon ;

Bennis, Mehdi ;

Kim, Seong-Lyun ;

Debbah, Merouane .

PROCEEDINGS OF THE IEEE, 2021, 109 (05) :796-819

← 1 2 →