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Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
KTH, Skolan för informations- och kommunikationsteknik (ICT). (COS)
KTH, Skolan för elektroteknik och datavetenskap (EECS).
KTH, Skolan för elektroteknik och datavetenskap (EECS).
2019 (engelsk)Inngår i: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 57, nr 3, s. 42-48Artikkel i tidsskrift (Fagfellevurdert) Accepted
Abstract [en]

URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a coexistence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose 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 URLLC 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 benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and non-scheduled traffic types is satisfied.

sted, utgiver, år, opplag, sider
2019. Vol. 57, nr 3, s. 42-48
Emneord [en]
5G, non-scheduled traffic, IoT, machine learning, proactive resource provisioning, URLLC
Emneord [fa]
نسل پنجم، مخابرات امن، مخابرات قابل اطمینان، مخابرات سلولی، مخام، مخابرات ماشین به ماشین، تخصیص منبع، متعامد، غیر متعامد.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-240665DOI: 10.1109/MCOM.2019.1800610ISI: 000461239300007Scopus ID: 2-s2.0-85062987555OAI: oai:DiVA.org:kth-240665DiVA, id: diva2:1274338
Merknad

QC 20190107

Tilgjengelig fra: 2018-12-30 Laget: 2018-12-30 Sist oppdatert: 2019-04-30bibliografisk kontrollert

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