Energy-efficient semi-supervised learning framework for subchannel allocation in non-orthogonal multiple access systems

Autor: S. Devipriya, J. Martin Leo Manickam, B. Victoria Jancee
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: ETRI Journal, Vol 45, Iss 6, Pp 963-973 (2023)
Druh dokumentu: article
ISSN: 2022-0251
1225-6463
2233-7326
DOI: 10.4218/etrij.2022-0251
Popis: 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.
Databáze: Directory of Open Access Journals
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