Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Serving Non-Scheduled URLLC Traffic: Challenges and Learning-Powered Strategies
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab). (COS)
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Whilst enabling URLLC is essential for realizing many promising 5G applications, the design of communications' solutions for serving such unseen type of traffic with stringent delay and reliability requirements is in its infancy. In prior studies, physical and MAC layer solutions for assuring the end-to-end delay requirement of scheduled URLLC traffic have been investigated. However, there is lack of study on enabling non-scheduled transmission of urgent URLLC traffic, especially in coexistence with the scheduled URLLC traffic. This study at first sheds light into the coexistence design challenges, especially the radio resource management (RRM) problem. It also leverages recent advances in machine learning (ML) to exploit spatial/temporal correlation in user behaviors and use of radio  resources, and proposes a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the interplay between the reliabilities of coexisting traffic, uncertainties in users' demands and channel conditions, and amount of required radio resources.

Keywords [en]
5G, scheduled/non-scheduled coexistence, IoT, machine learning, proactive resource provisioning, URLLC.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-237452OAI: oai:DiVA.org:kth-237452DiVA, id: diva2:1259198
Note

QC 20181029

Available from: 2018-10-28 Created: 2018-10-28 Last updated: 2019-02-07Bibliographically approved
In thesis
1. Serving IoT Communications over Cellular Networks: Challenges and Solutions in Radio Resource Management for Massive and Critical IoT Communications
Open this publication in new window or tab >>Serving IoT Communications over Cellular Networks: Challenges and Solutions in Radio Resource Management for Massive and Critical IoT Communications
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Internet of Things (IoT) communications refer to the interconnections of smart devices, with reduced human intervention, which enable them to participate more actively in everyday life. It is expected that introduction of a scalable, energy efficient, and reliable IoT connectivity solution can bring enormous benefits to the society, especially in healthcare, wellbeing, and smart homes and industries. In the last two decades, there have been efforts in academia and industry to enable IoT connectivity over the legacy communications infrastructure. In recent years, it is becoming more and more clear that the characteristics and requirements of the IoT traffic are way different from the legacy traffic originating from existing communications services like voice and web surfing, and hence, IoT-specific communications systems and protocols have received profound attention. Until now, several revolutionary solutions, including cellular narrowband-IoT, SigFox, and LoRaWAN, have been proposed/implemented. As each of these solutions focuses on a subset of performance indicators at the cost of sacrificing the others, there is still lack of a dominant player in the market capable of delivering scalable, energy efficient, and reliable IoT connectivity. The present work is devoted to characterizing state-of-the-art technologies for enabling large-scale IoT connectivity, their limitations, and our contributions in performance assessment and enhancement for them. Especially, we focus on grant-free radio access and investigate its applications in supporting massive and critical IoT communications. The main contributions presented in this work include (a) developing an analytical framework for energy/latency/reliability assessment of IoT communications over grant-based and grant-free systems; (b) developing advanced RRM techniques for energy and spectrum efficient serving of massive and critical IoT communications, respectively; and (c) developing advanced data transmission/reception protocols for grant-free IoT networks. The performance evaluation results indicate that supporting IoT devices with stringent energy/delay constraints over limited radio resources calls for aggressive technologies breaking the barrier of the legacy interference-free orthogonal communications.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 91
Series
TRITA-EECS-AVL ; 2018:73
Keywords
5G, Battery lifetime, Grant-based and grant-free access, Massive and critical IoT communications, Radio resource manage
National Category
Engineering and Technology
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-238678 (URN)978-91-7729-973-8 (ISBN)
Public defence
2018-11-23, Sal C, Electrum, Kistagången 16, Kista., Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Ozger, Mustafa

Search in DiVA

By author/editor
Azari, AminCavdar, CicekOzger, Mustafa
By organisation
Radio Systems Laboratory (RS Lab)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 1305 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf