Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: (swepub) pers:(Ottersten Björn 1961) lar1:(kth) hsvcat:2 > (2015-2019) > Learning-Assisted O...

Details
MARC

You, LeiUppsala universitet,Avdelningen för datalogi (author)

Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems

Article/chapterEnglish2019

Publisher, publication year, extent ...

Institute of Electrical and Electronics Engineers (IEEE),2019
printrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:kth-258951
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258951URI
https://doi.org/10.1109/TGCN.2019.2902838DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424007URI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

QC 20191115
In this paper, we study a class of minimum-energy scheduling problems in non-orthogonal multiple access (NOMA) systems. NOMA is adopted to enable efficient channel utilization and interference mitigation, such that base stations can consume minimal energy to empty their queued data in presence of transmission deadlines, and each user can obtain all the requested data timely. Due to the high computational complexity in resource scheduling and the stringent execution-time constraints in practical systems, providing a time-efficient and high-quality solution to 5G real-time systems is challenging. The conventional iterative optimization approaches may exhibit their limitations in supporting online optimization. We herein explore a viable alternative and develop a learning-assisted optimization framework to improve the computational efficiency while retaining competitive energy-saving performance. The idea is to use deep-learning-based predictions to accelerate the optimization process in conventional optimization methods for tackling the NOMA resource scheduling problems. In numerical studies, the proposed optimization framework demonstrates high computational efficiency. Its computational time is insensitive to the input size. The framework is able to provide optimal solutions as long as the learning-based predictions satisfy a derived optimality condition. For the general cases with imperfect predictions, the algorithmic solution is error-tolerable and performance scaleable, leading the energy-saving performance close to the global optimum.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
NOMA
Optimization
Resource management
Real-time systems
Heuristic algorithms
Computational complexity
Data communication
Non-orthogonal multiple access
deep neural network
energy optimization
resource scheduling

Added entries (persons, corporate bodies, meetings, titles ...)

You, L. (author)
He, QingKTH,Nätverk och systemteknik,Royal Institute of Technology(Swepub:kth)u1bhfqyt (author)
Vu, T. X.University of Luxembourg (author)
Chatzinotas, S.University of Luxembourg (author)
Yuan, DiUppsala universitet,Avdelningen för datalogi(Swepub:uu)diayu642 (author)
Ottersten, Björn,1961-University of Luxembourg, Luxembourg City, 1855, Luxembourg,Signal Processing(Swepub:kth)u120hb53 (author)
Uppsala universitetAvdelningen för datalogi (creator_code:org_t)

Related titles

In:IEEE Transactions on Green Communications and Networking: Institute of Electrical and Electronics Engineers (IEEE)3:3, s. 615-6272473-2400

Internet link

Find in a library

IEEE Transactions on Green Communications and Networking (Search for host publication in LIBRIS)

To the university's database

Find more in SwePub

By the author/editor: You, Lei; You, L.; He, Qing; Vu, T. X.; Chatzinotas, S.; Yuan, Di; show more...; Ottersten, Björn ...; show less...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Signal Processin ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

Articles in the publication: IEEE Transaction ...

By the university: Royal Institute of Technology; Uppsala University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se