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Efficient Minimum-Energy Scheduling with Machine-Learning Based Predictions for Multiuser MISO Systems
University of Luxembourg, Luxembourg.
University of Luxembourg, Luxembourg.
Uppsala University, Sweden.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0019-8411
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2018 (English)In: 2018 IEEE International Conference on Communications (ICC), 2018, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and energy-efficient data transmission, such that all the users' demand can be delivered within a limited time. The high computational complexity in optimal iterative algorithms limits their applications in real-time network operations. In this paper, we rethink the conventional optimization algorithms, and embed machine-learning based predictions in the optimization process, aiming at improving the computational efficiency and meeting the stringent execution-time limits in practice, while retaining competitive energy-saving performance for the MISO system. Numerical results demonstrate that the proposed method, i.e., optimization with machine- learning predictions (OMLP), is able to provide a time-efficient and high-quality solution for the considered scheduling problem. Towards online scheduling in real-time communications, OMLP is of high computational efficiency compared to conventional optimal iterative algorithms. OMLP guarantees the optimality as long as the machine- learning based predictions are accurate.

Place, publisher, year, edition, pages
2018. p. 1-6
Series
IEEE International Conference on Communications (ICC), E-ISSN 1938-1883
Keywords [en]
computational complexity;energy conservation;iterative methods;learning (artificial intelligence);MISO communication;multi-access systems;optimisation;telecommunication computing;telecommunication power management;telecommunication scheduling;efficient minimum-energy scheduling;multiuser MISO systems;energy-efficient scheduling problem;multiple-input single-output systems;stringent execution-time requirements;optimal user-group scheduling;energy-efficient data transmission;high computational complexity;real-time network operations;optimization process;competitive energy-saving performance;MISO system;OMLP;high-quality solution;real-time communications;high computational efficiency;conventional optimal iterative algorithms;optimization algorithms;scheduling problem;optimization with machine learning predictions;Processor scheduling;Optimal scheduling;Scheduling;Machine learning;Data communication;Real-time systems
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-151025DOI: 10.1109/ICC.2018.8422520ISBN: 978-1-5386-3180-5 (electronic)ISBN: 978-1-5386-3181-2 (print)OAI: oai:DiVA.org:liu-151025DiVA, id: diva2:1250229
Conference
2018 IEEE International Conference on Communications (ICC), 20-24 May 2018, Kansas City, USA
Available from: 2018-09-22 Created: 2018-09-22 Last updated: 2018-09-25

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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