ETRI Journal

  • 제45권6호
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  • Pages.963-973
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  • 2023
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Energy-efficient semi-supervised learning framework for subchannel allocation in non-orthogonal multiple access systems

  • S. Devipriya (Department of Electronics and Communication Engineering, St. Joseph's College of Engineering) ;
  • J. Martin Leo Manickam (Department of Electronics and Communication Engineering, St. Joseph's College of Engineering) ;
  • B. Victoria Jancee (Department of Electronics and Communication Engineering, St. Joseph's College of Engineering)
  • 투고 : 2022.06.23
  • 심사 : 2022.11.28
  • 발행 : 2023.12.10
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초록

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

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참고문헌

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