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Learning-Assisted O...
Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems
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- You, Lei (author)
- Uppsala universitet,Avdelningen för datalogi
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You, L. (author)
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- He, Qing (author)
- KTH,Nätverk och systemteknik,Royal Institute of Technology
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- Vu, T. X. (author)
- University of Luxembourg
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- Chatzinotas, S. (author)
- University of Luxembourg
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- Yuan, Di (author)
- Uppsala universitet,Avdelningen för datalogi
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- Ottersten, Björn, 1961- (author)
- University of Luxembourg, Luxembourg City, 1855, Luxembourg,Signal Processing
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2019
- 2019
- English.
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In: IEEE Transactions on Green Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2400. ; 3:3, s. 615-627
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Abstract
Subject headings
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- 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
- 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)
Keyword
- NOMA
- Optimization
- Resource management
- Real-time systems
- Heuristic algorithms
- Computational complexity
- Data communication
- Non-orthogonal multiple access
- deep neural network
- energy optimization
- resource scheduling
Publication and Content Type
- ref (subject category)
- art (subject category)
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