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Efficient Minimum-E...
Efficient Minimum-Energy Scheduling with Machine-Learning Based Predictions for Multiuser MISO Systems
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- You, Lei (författare)
- Uppsala universitet,Datalogi,Optimisation,Uppsala University, Sweden
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- Vu, Thang X (författare)
- University of Luxembourg, Luxembourg,Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Esch Sur Alzette, Luxembourg
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- You, Lei (författare)
- Uppsala University, Sweden
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- Fowler, Scott (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten,Linkoping Univ, Dept Sci & Technol, Linkoping, Sweden
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- Yuan, Di (författare)
- Uppsala universitet,Datalogi,Optimisation,Uppsala University, Sweden
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- Lei, Lei (författare)
- University of Luxembourg, Luxembourg
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(creator_code:org_t)
- IEEE, 2018
- 2018
- Engelska.
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Ingår i: 2018 IEEE International Conference on Communications (ICC). - : IEEE. - 9781538631805 - 9781538631812 ; , s. 1-6
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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