QNN framework based multiclass classification for downlink NOMA detectors

Quantum neural networks (QNNs) have attracted significant attention recently, primarily because of their potential to address complex problems deemed difficult for traditional computational methods. This study explores the viability of QNN in handling multiclass classification tasks in downlink nono...

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Bibliographic Details
Published inJournal of communications and networks Vol. 27; no. 4; pp. 231 - 240
Main Authors Lee, Hye Yeong, Lee, Man Hee, Shin, Soo Young
Format Journal Article
LanguageEnglish
Published 한국통신학회 01.08.2025
Subjects
전자/정보통신공학
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ISSN1229-2370
1976-5541
DOI10.23919/JCN.2025.000045

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Summary:Quantum neural networks (QNNs) have attracted significant attention recently, primarily because of their potential to address complex problems deemed difficult for traditional computational methods. This study explores the viability of QNN in handling multiclass classification tasks in downlink nonorthogonal multiple access (NOMA) frameworks. The investigation includes a design of QNN framework and performance evaluation of a QNN-based NOMA detector, integrating maximum likelihood (ML), successive interference cancellation (SIC), and rotated ML (RML) methods. A QNN framework was configured for all three detectors, and a comparative analysis was conducted in terms of loss, accuracy, and testing across varied signal-to-noise ratio (SNR) levels and power allocation coefficients, considering NOMA-specific characteristics. Furthermore, the computational complexity of each detector was analyzed within the proposed framework. KCI Citation Count: 0
ISSN:1229-2370
1976-5541
DOI:10.23919/JCN.2025.000045